{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6455b906",
   "metadata": {},
   "source": [
    "## 导入所需的包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e0f7031",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-19T03:12:44.361941Z",
     "start_time": "2021-10-19T03:10:43.736626Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fatal: unable to access 'https://github.com/4paradigm/autox.git/': Empty reply from server\r\n"
     ]
    }
   ],
   "source": [
    "!git pull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38a3e5db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-19T03:12:44.766394Z",
     "start_time": "2021-10-19T03:12:44.365844Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mcommit ed01e064817616c76610b765b67f70a449206c2e\u001b[m\u001b[33m (\u001b[m\u001b[1;36mHEAD -> \u001b[m\u001b[1;32mmaster\u001b[m\u001b[33m, \u001b[m\u001b[1;31morigin/master\u001b[m\u001b[33m, \u001b[m\u001b[1;31morigin/HEAD\u001b[m\u001b[33m)\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Oct 19 11:18:10 2021 +0800\r\n",
      "\r\n",
      "    log1p\r\n",
      "\r\n",
      "\u001b[33mcommit d82b523e99c59938bbac626825eafb81f873d56f\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Oct 18 17:21:06 2021 +0800\r\n",
      "\r\n",
      "    调参设置为false.\r\n",
      "\r\n",
      "\u001b[33mcommit 9501dc35a4cc479e9309f92085966e2d41892048\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Oct 18 17:08:22 2021 +0800\r\n",
      "\r\n",
      "    modify xgboost parameters.\r\n",
      "\r\n",
      "\u001b[33mcommit 9a07bb9af8dd03d28f170729ab375c8d08049ec7\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Oct 18 15:27:07 2021 +0800\r\n",
      "\r\n",
      "    debug: MSE的计算.\r\n",
      "\r\n",
      "\u001b[33mcommit eca729a754ad975a367fc901d8da56e1bfdf7659\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Oct 18 15:22:26 2021 +0800\r\n",
      "\r\n",
      "    优化xgb模型的log.\r\n",
      "\r\n",
      "\u001b[33mcommit c1d3c3fb2669f6eb3613de6753048a784adb4d51\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Oct 18 09:10:36 2021 +0800\r\n",
      "\r\n",
      "    regressor, 增加metric配置.\r\n",
      "\r\n",
      "\u001b[33mcommit 96fb99defa13a3f379a56220984995a84c07fadb\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Oct 18 08:38:59 2021 +0800\r\n",
      "\r\n",
      "    优化pipeline, 特征合并.\r\n",
      "\r\n",
      "\u001b[33mcommit 39373c28489f349937c8b0c56cc3825dcc885f83\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Oct 18 08:30:27 2021 +0800\r\n",
      "\r\n",
      "    更新pipeline.\r\n",
      "\r\n",
      "\u001b[33mcommit b2637e3039460d09224abd0d9e7ec129be625965\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Oct 17 13:50:59 2021 +0800\r\n",
      "\r\n",
      "    add case: Allstate.\r\n",
      "\r\n",
      "\u001b[33mcommit 2f39a6de3d26508ac694db8ddfe38d5a70da414f\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Oct 17 10:44:58 2021 +0800\r\n",
      "\r\n",
      "    debug feature: fe_time.\r\n",
      "\r\n",
      "\u001b[33mcommit 6c07c52fd8aad4cc83e8eb5acebe6df2954e3189\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Oct 17 10:43:48 2021 +0800\r\n",
      "\r\n",
      "    debug, fe_time.\r\n",
      "\r\n",
      "\u001b[33mcommit c63350a2ed12c0018df3e03bb6f03d15654e9982\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Oct 17 10:39:56 2021 +0800\r\n",
      "\r\n",
      "    add feature: fe_time.\r\n",
      "\r\n",
      "\u001b[33mcommit 9237f8571dae8f221d9ea31e27b40a1d58de8cf5\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Oct 17 10:28:03 2021 +0800\r\n",
      "\r\n",
      "    debug.\r\n",
      "\r\n",
      "\u001b[33mcommit 83cec4c0a6eb8377076ac351e4704dc29feeff55\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Oct 17 10:24:01 2021 +0800\r\n",
      "\r\n",
      "    debug datetime feature type.\r\n",
      "\r\n",
      "\u001b[33mcommit 926144780b3966e039e165096427f3c413ff8c0e\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Oct 17 10:21:07 2021 +0800\r\n",
      "\r\n",
      "    debug detect datetime feature type.\r\n",
      "\r\n",
      "\u001b[33mcommit 74abc938221fd49d313ffd5cb66318d2cbe154af\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sat Oct 16 21:29:51 2021 +0800\r\n",
      "\r\n",
      "    debug.\r\n",
      "\r\n",
      "\u001b[33mcommit 21f25e9a66a05caac6ebeb3ae9314a88edd9116a\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sat Oct 16 18:00:42 2021 +0800\r\n",
      "\r\n",
      "    重命名1-1拼表特征的列名\r\n",
      "\r\n",
      "\u001b[33mcommit 0c1cf483685645f6ceeea03fe1f6701cffa4850e\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sat Oct 16 08:18:18 2021 +0800\r\n",
      "\r\n",
      "    add grocery_sales results and demos.\r\n",
      "\r\n",
      "\u001b[33mcommit bb8deaa13fbd261e8dc98f04dc2574b05b985bcb\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Oct 8 20:07:45 2021 +0800\r\n",
      "\r\n",
      "    增加ventilator和Santander上分点总结；更新ventilator结果；\r\n",
      "\r\n",
      "\u001b[33mcommit de84f292155bce87fe44469502ba9263de934be1\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Sep 30 08:20:04 2021 +0800\r\n",
      "\r\n",
      "    优化diff和shift特征\r\n",
      "\r\n",
      "\u001b[33mcommit 50ab24fbcff59134d874e461652b92f60aa402d0\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Sep 30 08:19:38 2021 +0800\r\n",
      "\r\n",
      "    add cumsum feature\r\n",
      "\r\n",
      "\u001b[33mcommit 91f4e68e545fc91284219855cffc2e7c679ce087\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Sep 29 20:44:05 2021 +0800\r\n",
      "\r\n",
      "    debug for denoising autoencoder.\r\n",
      "\r\n",
      "\u001b[33mcommit d9870fb170c6f837818ff406d13fe25634d44a8a\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Sep 29 20:33:36 2021 +0800\r\n",
      "\r\n",
      "    add shift feature; add diff featuers; add ventilator demos.\r\n",
      "\r\n",
      "\u001b[33mcommit 70a9128f32a988454c49651716b7afbd109fa929\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Sep 24 19:51:55 2021 +0800\r\n",
      "\r\n",
      "    updata santander result.\r\n",
      "\r\n",
      "\u001b[33mcommit db903d295e59e38c749ac45381347561caf40545\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Sep 24 17:18:50 2021 +0800\r\n",
      "\r\n",
      "    init FeatureDenoisingAutoencoder.\r\n",
      "\r\n",
      "\u001b[33mcommit 08cc35212f2e0d463724cc6b27597253f1329105\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Sep 24 17:13:57 2021 +0800\r\n",
      "\r\n",
      "    denoising autoencoder特征.\r\n",
      "\r\n",
      "\u001b[33mcommit 410ef74d5756873d890caa07274b8ee0e1d746c5\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Sep 23 16:58:48 2021 +0800\r\n",
      "\r\n",
      "    modify README.md; add stumbleupon demo.\r\n",
      "\r\n",
      "\u001b[33mcommit 755a5bc45c760238e6e4309f1985959a4c5364b1\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Sep 22 11:37:03 2021 +0800\r\n",
      "\r\n",
      "    全流程中加入nlp特征; 增加stumbleupon的demo.\r\n",
      "\r\n",
      "\u001b[33mcommit 4d39f318551660fe139878a5c88db1d3aaac0b97\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Sep 17 20:53:19 2021 +0800\r\n",
      "\r\n",
      "    一键执行逻辑中增加nlp特征.\r\n",
      "\r\n",
      "\u001b[33mcommit 55f67716c7a50366d45249c0f9d3ea4166c13c41\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Sep 17 20:48:51 2021 +0800\r\n",
      "\r\n",
      "    1. 增加nlp特征; 2. 增加StumbleUpon案例结果.\r\n",
      "\r\n",
      "\u001b[33mcommit 74f9d849c0eadde63ff71ec5a79631d3af08fd7b\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Sep 17 16:05:38 2021 +0800\r\n",
      "\r\n",
      "    增加文本类型.\r\n",
      "\r\n",
      "\u001b[33mcommit e45f0b7c3855514177328519b574e7b969812ce4\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Sep 15 11:08:54 2021 +0800\r\n",
      "\r\n",
      "    modify README.md\r\n",
      "\r\n",
      "\u001b[33mcommit 7d4b34cc7cc584b7643d17b7c0bca7c8f92dbf7b\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Sep 14 17:19:09 2021 +0800\r\n",
      "\r\n",
      "    debug: 分解特征.\r\n",
      "\r\n",
      "\u001b[33mcommit 77bc1761c50bda21fce5bc21fff4115dd3d11443\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Sep 14 16:08:56 2021 +0800\r\n",
      "\r\n",
      "    init FeatureDimensionReduction.\r\n",
      "\r\n",
      "\u001b[33mcommit f8f277e7b8cd314127bb622484855fe8aac78cf5\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Sep 14 15:37:52 2021 +0800\r\n",
      "\r\n",
      "    降维特征.\r\n",
      "\r\n",
      "\u001b[33mcommit f56b86583be17aac48f69067644bec8bf624b57b\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Sep 14 11:26:25 2021 +0800\r\n",
      "\r\n",
      "    debug: xbg二分类模型改成预测概率而非硬分类.\r\n",
      "\r\n",
      "\u001b[33mcommit ff8c20b9742ad36c31ab5baec8c1f1211c61e8bf\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Sep 14 10:49:10 2021 +0800\r\n",
      "\r\n",
      "    add demo: kaggle springleaf\r\n",
      "\r\n",
      "\u001b[33mcommit ac4dcab227d8bcc17ecc2d69e5e0486a5f13d5f7\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Sep 14 10:37:49 2021 +0800\r\n",
      "\r\n",
      "    modify README.md: 更新springleaf一键执行结果.\r\n",
      "\r\n",
      "\u001b[33mcommit 26eb6bea33b8e8428ee1afcd4403ccae2948724e\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Sep 10 19:10:12 2021 +0800\r\n",
      "\r\n",
      "    debug: task_type\r\n",
      "\r\n",
      "\u001b[33mcommit 58820caad3acc6d2b1fae7a81e051d3fb30f13d3\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Sep 10 14:35:18 2021 +0800\r\n",
      "\r\n",
      "    优化log\r\n",
      "\r\n",
      "\u001b[33mcommit 51704abfd80114578eab318356cc77b1ef46e18b\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Sep 10 14:30:35 2021 +0800\r\n",
      "\r\n",
      "    1. 增加case: kaggle springleaf;\r\n",
      "    2. 优化autox get_submit逻辑\r\n",
      "\r\n",
      "\u001b[33mcommit 6455e62326d344b33a37f100b4fecf2dcb637c8a\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Sep 2 16:50:56 2021 +0800\r\n",
      "\r\n",
      "    增加ieee结果和pipeline demo.\r\n",
      "\r\n",
      "\u001b[33mcommit 74d679c47ae2e0639d02b994e6cf1f6f84dfe560\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Sep 2 15:44:15 2021 +0800\r\n",
      "\r\n",
      "    debug for feature_filter.\r\n",
      "\r\n",
      "\u001b[33mcommit 75c9510e049cfdbaa57f07b3f4306f1a161fccea\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Sep 2 14:48:42 2021 +0800\r\n",
      "\r\n",
      "    优化groupby key筛选条件.\r\n",
      "\r\n",
      "\u001b[33mcommit ff2bb3fb04a5b84feca94b26de7ac6048cc36c7b\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Sep 1 17:37:03 2021 +0800\r\n",
      "\r\n",
      "    debug: fe_rank\r\n",
      "\r\n",
      "\u001b[33mcommit 1fe1f1732606a5dbf007270c2dbae1711b5a72b6\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Sep 1 16:27:16 2021 +0800\r\n",
      "\r\n",
      "    debug: 拼接1-1简单表.\r\n",
      "\r\n",
      "\u001b[33mcommit c7b7964fb2713118d6e85d0ef22a384f924143be\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Sep 1 16:04:23 2021 +0800\r\n",
      "\r\n",
      "    增加功能，拼接1-1简单表;\r\n",
      "    kaggle_ieee, demo;\r\n",
      "    modify README.md.\r\n",
      "\r\n",
      "\u001b[33mcommit 21457fafb8d01644cfc668d0aab8d463a8cda3e7\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 31 15:45:24 2021 +0800\r\n",
      "\r\n",
      "    modify README\r\n",
      "\r\n",
      "\u001b[33mcommit 2c2cf54574a8a9c6c21f5452ed5f5bcf4b3ae7ef\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 31 15:25:09 2021 +0800\r\n",
      "\r\n",
      "    modify README_EN.md\r\n",
      "\r\n",
      "\u001b[33mcommit 3da3ba229d78d81d844226c0d584d1da6572109a\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 30 20:34:13 2021 +0800\r\n",
      "\r\n",
      "    init Fe_rank.\r\n",
      "\r\n",
      "\u001b[33mcommit 7f2e3717b84ebdd223037ce2ac63740d46571a9a\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 30 17:32:12 2021 +0800\r\n",
      "\r\n",
      "    add rank feature.\r\n",
      "\r\n",
      "\u001b[33mcommit b3fa6719c0052b964f0d74a6bf9a8941c488d915\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 30 10:43:40 2021 +0800\r\n",
      "\r\n",
      "    add demo: kaggle house price.\r\n",
      "\r\n",
      "\u001b[33mcommit 59b7d261d059f84a291fda6013f7eeffdcae9987\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Aug 29 08:03:49 2021 +0800\r\n",
      "\r\n",
      "    modify README_EN.md, 跳转链接.\r\n",
      "\r\n",
      "\u001b[33mcommit f2581a891bc919b3c0f11fb4c7cf700ecedc2f73\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Aug 29 07:57:44 2021 +0800\r\n",
      "\r\n",
      "    modify README_EN.md\r\n",
      "\r\n",
      "\u001b[33mcommit 50b186979fa431cdfaed38a508f1a134d2e7e0f1\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 27 17:44:44 2021 +0800\r\n",
      "\r\n",
      "    modify README.md, 新增kaggle house price数据集.\r\n",
      "\r\n",
      "\u001b[33mcommit 08e2dc8e069ffdd5f5dea5870af971d7b2cbe1df\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 27 16:39:01 2021 +0800\r\n",
      "\r\n",
      "    install_requires, 忽略tabnet.\r\n",
      "\r\n",
      "\u001b[33mcommit 89611b6d10d4492cde5b6f390d2c4077977b66f1\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 27 15:52:55 2021 +0800\r\n",
      "\r\n",
      "    xgb打印轮次设置为100\r\n",
      "\r\n",
      "\u001b[33mcommit 984d81a49150edd80d177137ceb748591db1a04d\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 27 15:42:14 2021 +0800\r\n",
      "\r\n",
      "    回归模型调参,修改验证集切分方式.\r\n",
      "\r\n",
      "\u001b[33mcommit 7fb12ebf09d04b0a617896ab1a63474fc8bb55a5\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 27 15:09:13 2021 +0800\r\n",
      "\r\n",
      "    优化特征类型识别.\r\n",
      "\r\n",
      "\u001b[33mcommit 39f94e1e4cd82c6d148556d80bb47146bfc8d539\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 27 14:58:53 2021 +0800\r\n",
      "\r\n",
      "    优化特征类型识别.\r\n",
      "\r\n",
      "\u001b[33mcommit 9f78099656c32f335a9134ea6358f131891699f0\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 26 17:02:52 2021 +0800\r\n",
      "\r\n",
      "    modify readme.\r\n",
      "\r\n",
      "\u001b[33mcommit 656c91218b289fe61cf21855e16b6895acec2c78\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 24 15:21:30 2021 +0800\r\n",
      "\r\n",
      "    优化readme,增加zhidemai比赛上分点总结\r\n",
      "\r\n",
      "\u001b[33mcommit 0aa3748f2a06d3639f6afeb94e33bca7d0bdeea8\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 20 14:33:54 2021 +0800\r\n",
      "\r\n",
      "    modify README.md\r\n",
      "\r\n",
      "\u001b[33mcommit 0cfd6d5cf86fee9b1a02b79a94ffd97c0b8a166a\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 19 20:14:02 2021 +0800\r\n",
      "\r\n",
      "    setup安装包增加tabnet.\r\n",
      "\r\n",
      "\u001b[33mcommit 84af1a14acd1e121bfcae1afde06f00b80df614d\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Aug 18 11:52:08 2021 +0800\r\n",
      "\r\n",
      "    debug: tabnet的调参参数配置\r\n",
      "\r\n",
      "\u001b[33mcommit d185a546260127b5faffab105d3a2c0eaafb69bc\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 17 21:19:57 2021 +0800\r\n",
      "\r\n",
      "    tabnet, reshape y\r\n",
      "\r\n",
      "\u001b[33mcommit 388c00372b8f4af10bed4212ccc1bdf6e3f54275\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 17 21:01:59 2021 +0800\r\n",
      "\r\n",
      "    debug, tabnet.\r\n",
      "\r\n",
      "\u001b[33mcommit 18bc69af5802750f5ec23312e7ab649ddc25cfa8\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 17 20:25:31 2021 +0800\r\n",
      "\r\n",
      "    tabnet: 缺失值用中位数填充.\r\n",
      "\r\n",
      "\u001b[33mcommit 5a88c0ff98338dddb8d1406eb1bfd0d2a72f2121\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 17 19:37:39 2021 +0800\r\n",
      "\r\n",
      "    优化tabnet\r\n",
      "\r\n",
      "\u001b[33mcommit 1e07509db81c4c6e3222ae6697ef5c119a2eef31\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 17 16:08:43 2021 +0800\r\n",
      "\r\n",
      "    bagging中增加tabnet模型\r\n",
      "\r\n",
      "\u001b[33mcommit fbed7f9fb73e4a9ac143398902fd042a6fa54247\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 17 16:05:36 2021 +0800\r\n",
      "\r\n",
      "    tabnet regressor\r\n",
      "\r\n",
      "\u001b[33mcommit 8e89749c53b5ebddc86cb9b8ace762f7b3854841\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 17 15:21:32 2021 +0800\r\n",
      "\r\n",
      "    debug模式下缩短调参时间。\r\n",
      "\r\n",
      "\u001b[33mcommit 9c70574e38194766cbecca0822b2cdd48867144b\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 17 15:13:04 2021 +0800\r\n",
      "\r\n",
      "    debug模型打印日志.\r\n",
      "\r\n",
      "\u001b[33mcommit 7b18599f132699f056470761701968931da3f7a9\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Tue Aug 17 15:04:15 2021 +0800\r\n",
      "\r\n",
      "    增加debug模式，方便快速调试.\r\n",
      "\r\n",
      "\u001b[33mcommit d2d332b0bb5432e0ef49df01455c81a2644e7271\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 16 08:00:11 2021 +0800\r\n",
      "\r\n",
      "    auto_label_encoder,设置silence_cols\r\n",
      "\r\n",
      "\u001b[33mcommit 1b8ced5337d1826ff6dadec235c8cc5a00cb4e89\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sun Aug 15 08:42:35 2021 +0800\r\n",
      "\r\n",
      "    内存优化.\r\n",
      "\r\n",
      "\u001b[33mcommit fab33ba59407c96ac146c4ad6865a32f06b8fa34\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Aug 11 10:53:16 2021 +0800\r\n",
      "\r\n",
      "    增加二分类模型.\r\n",
      "\r\n",
      "\u001b[33mcommit 373c58eb950fbc364581d75b493ecaa1735079ed\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 9 15:28:00 2021 +0800\r\n",
      "\r\n",
      "    识别任务类型\r\n",
      "\r\n",
      "\u001b[33mcommit 44755fa33a1a6f59239786ab80de9d521c72b68c\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 6 16:19:49 2021 +0800\r\n",
      "\r\n",
      "    lgb, Verbose = 100\r\n",
      "\r\n",
      "\u001b[33mcommit e18d2dd86b63d4e253b3ad67017aeb82546cead3\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 6 13:21:03 2021 +0800\r\n",
      "\r\n",
      "    优化CrossXgbRegression.\r\n",
      "\r\n",
      "\u001b[33mcommit 6264775e9faec6d832cdb59819bca6534ced7401\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 6 11:41:03 2021 +0800\r\n",
      "\r\n",
      "    优化CrossXgbRegression: X进行StandardScaler, debug.\r\n",
      "\r\n",
      "\u001b[33mcommit 53135e94a4a0a0b079bf83df3db8e687e5ce0dc5\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 6 11:20:31 2021 +0800\r\n",
      "\r\n",
      "    优化CrossXgbRegression: X进行StandardScaler\r\n",
      "\r\n",
      "\u001b[33mcommit f0ac9242246a87dcee3e660c971cc33c5246f0bb\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Fri Aug 6 10:38:23 2021 +0800\r\n",
      "\r\n",
      "    优化CrossXgbRegression\r\n",
      "\r\n",
      "\u001b[33mcommit 75156d600497bb0910a8026aa87b2e7dc964ba79\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 5 22:34:31 2021 +0800\r\n",
      "\r\n",
      "    xgb model: tree_method='gpu_hist'\r\n",
      "\r\n",
      "\u001b[33mcommit afb229aa554f632badee1a171c28c991968eb331\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 5 20:58:11 2021 +0800\r\n",
      "\r\n",
      "    模型部分使用xgb和lgb融合\r\n",
      "\r\n",
      "\u001b[33mcommit 338ee3069d797cc10fe6d0f8a38afdc308cfdc71\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 5 20:29:23 2021 +0800\r\n",
      "\r\n",
      "    del temp.py\r\n",
      "\r\n",
      "\u001b[33mcommit c0dbe0887b53f7e8f02a6a3e9bcde803059ae973\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 5 19:53:05 2021 +0800\r\n",
      "\r\n",
      "    debug: X.iloc\r\n",
      "\r\n",
      "\u001b[33mcommit 2b806aedc6a2086268b1dadac4111ef1b1b1d83b\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 5 19:23:17 2021 +0800\r\n",
      "\r\n",
      "    debug: xgb regressor\r\n",
      "\r\n",
      "\u001b[33mcommit 5afbd56bdfb29e33d52d908a03c85508ad4e3d08\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 5 17:13:38 2021 +0800\r\n",
      "\r\n",
      "    xgboost不使用gpu_hist\r\n",
      "\r\n",
      "\u001b[33mcommit 1c56af83e7f90ec4fcc594fd87dcd0f6b9abaf8c\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 5 17:10:11 2021 +0800\r\n",
      "\r\n",
      "    xgboost不使用gpu\r\n",
      "\r\n",
      "\u001b[33mcommit b25c37c96115d5845841df1781eabbf4345af621\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 5 16:55:16 2021 +0800\r\n",
      "\r\n",
      "    增加xgb模型.\r\n",
      "\r\n",
      "\u001b[33mcommit 9af12e41e9a0a3295e63a0fe17988261e63050ee\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Aug 5 11:02:14 2021 +0800\r\n",
      "\r\n",
      "    get_submit, 优化模型训练部分\r\n",
      "\r\n",
      "\u001b[33mcommit d127c4e4a9b17f0244ec14af11482db60c261d21\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Aug 4 16:29:34 2021 +0800\r\n",
      "\r\n",
      "    debug: log输出.\r\n",
      "\r\n",
      "\u001b[33mcommit 3178f49c0ae81f5d9ba084d5dad1563804f457a4\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Aug 4 15:24:57 2021 +0800\r\n",
      "\r\n",
      "    增加模型调参功能.\r\n",
      "\r\n",
      "\u001b[33mcommit eb97b2420853e8e0fddd55343c6029ee6da8b4b3\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Wed Aug 4 15:02:27 2021 +0800\r\n",
      "\r\n",
      "    debug: concat_train_test操作在自动特征类型识别之后.\r\n",
      "\r\n",
      "\u001b[33mcommit 3452d8831ebe8d33f717ad16452109680aa8ef1f\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 2 20:00:16 2021 +0800\r\n",
      "\r\n",
      "    调整target encoding的阈值.\r\n",
      "\r\n",
      "\u001b[33mcommit 0d66de573edd6485725d603620c1344bf41e6222\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 2 19:48:30 2021 +0800\r\n",
      "\r\n",
      "    debug:del_targetencoding_cols去重.\r\n",
      "\r\n",
      "\u001b[33mcommit dc4df8ef3a70a0532578ee6e043223e1f219b60d\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 2 19:45:35 2021 +0800\r\n",
      "\r\n",
      "    debug: del_targetencoding_cols去重.\r\n",
      "\r\n",
      "\u001b[33mcommit 4ecb985b6d6b46145743338c2ed3bd28e3f7977f\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 2 19:34:08 2021 +0800\r\n",
      "\r\n",
      "    debug.\r\n",
      "\r\n",
      "\u001b[33mcommit a156ca854d341072d23df29623f82c819e5c5b81\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Aug 2 19:31:32 2021 +0800\r\n",
      "\r\n",
      "    target encoding特征筛选：test做了target encoding之后，有值的部分要大于90%\r\n",
      "\r\n",
      "\u001b[33mcommit ba93d457a017bb65bf3dc8d4676cea232a48c88c\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Jul 26 17:36:55 2021 +0800\r\n",
      "\r\n",
      "    内存优化, 优化log.\r\n",
      "\r\n",
      "\u001b[33mcommit 5cda17ae252012184192ba10ba941b7eabd1946d\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Jul 26 17:31:49 2021 +0800\r\n",
      "\r\n",
      "    内存优化.\r\n",
      "\r\n",
      "\u001b[33mcommit 0714e370615f919b92995b96eeea79dc475f1064\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Jul 26 14:52:50 2021 +0800\r\n",
      "\r\n",
      "    target encoding feature: 默认使用统计信息进行特征筛选\r\n",
      "\r\n",
      "\u001b[33mcommit cb928b678996a700a8e2c37ae725d5baab573558\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Jul 26 14:45:42 2021 +0800\r\n",
      "\r\n",
      "    target encoding feature: 优化统计信息筛选阈值\r\n",
      "\r\n",
      "\u001b[33mcommit e2a3e989e4b8b911663d871decfdcff05c818f45\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Mon Jul 26 14:39:50 2021 +0800\r\n",
      "\r\n",
      "    debug target encoding feature.\r\n",
      "\r\n",
      "\u001b[33mcommit 855c6c962ac0e9a716c9bc35441fee35bb89bf65\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Sat Jul 24 10:25:33 2021 +0800\r\n",
      "\r\n",
      "    add license file\r\n",
      "\r\n",
      "\u001b[33mcommit b5297ac2334e9b6d008d0e2d1c7a7e6b7dd61b78\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Jul 22 15:20:24 2021 +0800\r\n",
      "\r\n",
      "    modify README.md;\r\n",
      "    增加zhidemai_automl.ipynb.\r\n",
      "\r\n",
      "\u001b[33mcommit 8fb15db690010c060a11e3d28d5a9fdaa268113a\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Jul 22 14:36:09 2021 +0800\r\n",
      "\r\n",
      "    del sub files.\r\n",
      "\r\n",
      "\u001b[33mcommit 74ef3c0664934bb0033c178e50df9fee0986df55\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Jul 22 14:29:32 2021 +0800\r\n",
      "\r\n",
      "    first commit\r\n",
      "\r\n",
      "\u001b[33mcommit 4d75036cbf5db2927ba3233a9cdda4a32c022d85\u001b[m\r\n",
      "Author: poteman <946691288@qq.com>\r\n",
      "Date:   Thu Jul 22 14:26:45 2021 +0800\r\n",
      "\r\n",
      "    first commit\r\n"
     ]
    }
   ],
   "source": [
    "!git log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9185f791",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-19T03:12:48.975563Z",
     "start_time": "2021-10-19T03:12:44.769947Z"
    }
   },
   "outputs": [],
   "source": [
    "from autox import AutoX\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa24e429",
   "metadata": {},
   "source": [
    "## 配置数据信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cb67a86f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-19T03:12:48.985421Z",
     "start_time": "2021-10-19T03:12:48.977706Z"
    }
   },
   "outputs": [],
   "source": [
    "# 选择数据集\n",
    "data_name = 'allstate_claims'\n",
    "path = f'./data/{data_name}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b722824a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-19T03:13:11.844671Z",
     "start_time": "2021-10-19T03:12:48.986944Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "   INFO ->  [+] read sample_submission.csv\n",
      "   INFO ->  Memory usage of dataframe is 1.92 MB\n",
      "   INFO ->  Memory usage after optimization is: 0.60 MB\n",
      "   INFO ->  Decreased by 68.7%\n",
      "   INFO ->  table = sample_submission.csv, shape = (125546, 2)\n",
      "   INFO ->  [+] read train.csv\n",
      "   INFO ->  Memory usage of dataframe is 189.65 MB\n",
      "   INFO ->  Memory usage after optimization is: 27.70 MB\n",
      "   INFO ->  Decreased by 85.4%\n",
      "   INFO ->  table = train.csv, shape = (188318, 132)\n",
      "   INFO ->  [+] read test.csv\n",
      "   INFO ->  Memory usage of dataframe is 125.48 MB\n",
      "   INFO ->  Memory usage after optimization is: 17.88 MB\n",
      "   INFO ->  Decreased by 85.7%\n",
      "   INFO ->  table = test.csv, shape = (125546, 131)\n"
     ]
    }
   ],
   "source": [
    "autox = AutoX(target = 'loss', train_name = 'train.csv', test_name = 'test.csv', \n",
    "               id = ['id'], path = path, metric = 'mae')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc6898fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-19T09:28:05.510266Z",
     "start_time": "2021-10-19T03:13:11.846735Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "   INFO ->  start feature engineer\n",
      "   INFO ->  feature engineer: time\n",
      "   INFO ->  featureTime ops: []\n",
      "0it [00:00, ?it/s]\n",
      "   INFO ->  feature engineer: Cumsum\n",
      "   INFO ->  ignore featureCumsum\n",
      "   INFO ->  feature engineer: Shift\n",
      "   INFO ->  ignore featureShift\n",
      "   INFO ->  feature engineer: Diff\n",
      "   INFO ->  ignore featureDiff\n",
      "   INFO ->  feature engineer: Stat\n",
      "   INFO ->  ignore featureStat\n",
      "   INFO ->  feature engineer: NLP\n",
      "0it [00:00, ?it/s]\n",
      "   INFO ->  featureNlp ops: []\n",
      "   INFO ->  feature engineer: Count\n",
      "100%|██████████| 116/116 [00:36<00:00,  3.17it/s]\n",
      "   INFO ->  featureCount ops: [['cat1'], ['cat2'], ['cat3'], ['cat4'], ['cat5'], ['cat6'], ['cat7'], ['cat8'], ['cat9'], ['cat10'], ['cat11'], ['cat12'], ['cat13'], ['cat14'], ['cat15'], ['cat16'], ['cat17'], ['cat18'], ['cat19'], ['cat20'], ['cat21'], ['cat22'], ['cat23'], ['cat24'], ['cat25'], ['cat26'], ['cat27'], ['cat28'], ['cat29'], ['cat30'], ['cat31'], ['cat32'], ['cat33'], ['cat34'], ['cat35'], ['cat36'], ['cat37'], ['cat38'], ['cat39'], ['cat40'], ['cat41'], ['cat42'], ['cat43'], ['cat44'], ['cat45'], ['cat46'], ['cat47'], ['cat48'], ['cat49'], ['cat50'], ['cat51'], ['cat52'], ['cat53'], ['cat54'], ['cat55'], ['cat56'], ['cat57'], ['cat58'], ['cat59'], ['cat60'], ['cat61'], ['cat62'], ['cat63'], ['cat64'], ['cat65'], ['cat66'], ['cat67'], ['cat68'], ['cat69'], ['cat70'], ['cat71'], ['cat72'], ['cat73'], ['cat74'], ['cat75'], ['cat76'], ['cat77'], ['cat78'], ['cat79'], ['cat80'], ['cat81'], ['cat82'], ['cat83'], ['cat84'], ['cat85'], ['cat86'], ['cat87'], ['cat88'], ['cat89'], ['cat90'], ['cat91'], ['cat92'], ['cat93'], ['cat94'], ['cat95'], ['cat96'], ['cat97'], ['cat98'], ['cat99'], ['cat100'], ['cat101'], ['cat102'], ['cat103'], ['cat104'], ['cat105'], ['cat106'], ['cat107'], ['cat108'], ['cat109'], ['cat110'], ['cat111'], ['cat112'], ['cat113'], ['cat114'], ['cat115'], ['cat116']]\n",
      "   INFO ->  feature engineer: Rank\n",
      "100%|██████████| 28/28 [00:47<00:00,  1.71s/it]\n",
      "   INFO ->  featureRank ops: {'cat89': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat90': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat91': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat92': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat93': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat94': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat95': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat96': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat97': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat98': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat99': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat100': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat101': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat102': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat103': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat104': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat105': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat106': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat107': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat108': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat109': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat110': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat111': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat112': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat113': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat114': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat115': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}, 'cat116': {'id': ['rank'], 'cont1': ['rank'], 'cont2': ['rank'], 'cont3': ['rank'], 'cont4': ['rank'], 'cont5': ['rank'], 'cont6': ['rank'], 'cont7': ['rank'], 'cont8': ['rank'], 'cont9': ['rank'], 'cont10': ['rank'], 'cont11': ['rank'], 'cont12': ['rank'], 'cont13': ['rank'], 'cont14': ['rank'], 'loss': ['rank']}}\n",
      "100%|██████████| 132/132 [00:06<00:00, 19.56it/s]\n",
      "   INFO ->  label_encoder_list: ['cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9', 'cat10', 'cat11', 'cat12', 'cat13', 'cat14', 'cat15', 'cat16', 'cat17', 'cat18', 'cat19', 'cat20', 'cat21', 'cat22', 'cat23', 'cat24', 'cat25', 'cat26', 'cat27', 'cat28', 'cat29', 'cat30', 'cat31', 'cat32', 'cat33', 'cat34', 'cat35', 'cat36', 'cat37', 'cat38', 'cat39', 'cat40', 'cat41', 'cat42', 'cat43', 'cat44', 'cat45', 'cat46', 'cat47', 'cat48', 'cat49', 'cat50', 'cat51', 'cat52', 'cat53', 'cat54', 'cat55', 'cat56', 'cat57', 'cat58', 'cat59', 'cat60', 'cat61', 'cat62', 'cat63', 'cat64', 'cat65', 'cat66', 'cat67', 'cat68', 'cat69', 'cat70', 'cat71', 'cat72', 'cat73', 'cat74', 'cat75', 'cat76', 'cat77', 'cat78', 'cat79', 'cat80', 'cat81', 'cat82', 'cat83', 'cat84', 'cat85', 'cat86', 'cat87', 'cat88', 'cat89', 'cat90', 'cat91', 'cat92', 'cat93', 'cat94', 'cat95', 'cat96', 'cat97', 'cat98', 'cat99', 'cat100', 'cat101', 'cat102', 'cat103', 'cat104', 'cat105', 'cat106', 'cat107', 'cat108', 'cat109', 'cat110', 'cat111', 'cat112', 'cat113', 'cat114', 'cat115', 'cat116']\n",
      "   INFO ->  feature combination\n",
      "   INFO ->  shape of FE_all: (313864, 696), shape of train: (188318, 696), shape of test: (125546, 696)\n",
      "   INFO ->  feature filter\n",
      "100%|██████████| 696/696 [00:20<00:00, 33.59it/s] \n",
      "   INFO ->  filtered features: ['id', 'loss', 'cat89__loss__rank', 'cat90__loss__rank', 'cat91__loss__rank', 'cat92__loss__rank', 'cat93__loss__rank', 'cat94__loss__rank', 'cat95__loss__rank', 'cat96__loss__rank', 'cat97__loss__rank', 'cat98__loss__rank', 'cat99__loss__rank', 'cat100__loss__rank', 'cat101__loss__rank', 'cat102__loss__rank', 'cat103__loss__rank', 'cat104__loss__rank', 'cat105__loss__rank', 'cat106__loss__rank', 'cat107__loss__rank', 'cat108__loss__rank', 'cat109__loss__rank', 'cat110__loss__rank', 'cat111__loss__rank', 'cat112__loss__rank', 'cat113__loss__rank', 'cat114__loss__rank', 'cat115__loss__rank', 'cat116__loss__rank']\n",
      "   INFO ->  used_features: ['cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9', 'cat10', 'cat11', 'cat12', 'cat13', 'cat14', 'cat15', 'cat16', 'cat17', 'cat18', 'cat19', 'cat20', 'cat21', 'cat22', 'cat23', 'cat24', 'cat25', 'cat26', 'cat27', 'cat28', 'cat29', 'cat30', 'cat31', 'cat32', 'cat33', 'cat34', 'cat35', 'cat36', 'cat37', 'cat38', 'cat39', 'cat40', 'cat41', 'cat42', 'cat43', 'cat44', 'cat45', 'cat46', 'cat47', 'cat48', 'cat49', 'cat50', 'cat51', 'cat52', 'cat53', 'cat54', 'cat55', 'cat56', 'cat57', 'cat58', 'cat59', 'cat60', 'cat61', 'cat62', 'cat63', 'cat64', 'cat65', 'cat66', 'cat67', 'cat68', 'cat69', 'cat70', 'cat71', 'cat72', 'cat73', 'cat74', 'cat75', 'cat76', 'cat77', 'cat78', 'cat79', 'cat80', 'cat81', 'cat82', 'cat83', 'cat84', 'cat85', 'cat86', 'cat87', 'cat88', 'cat89', 'cat90', 'cat91', 'cat92', 'cat93', 'cat94', 'cat95', 'cat96', 'cat97', 'cat98', 'cat99', 'cat100', 'cat101', 'cat102', 'cat103', 'cat104', 'cat105', 'cat106', 'cat107', 'cat108', 'cat109', 'cat110', 'cat111', 'cat112', 'cat113', 'cat114', 'cat115', 'cat116', 'cont1', 'cont2', 'cont3', 'cont4', 'cont5', 'cont6', 'cont7', 'cont8', 'cont9', 'cont10', 'cont11', 'cont12', 'cont13', 'cont14', 'COUNT_cat1', 'COUNT_cat2', 'COUNT_cat3', 'COUNT_cat4', 'COUNT_cat5', 'COUNT_cat6', 'COUNT_cat7', 'COUNT_cat8', 'COUNT_cat9', 'COUNT_cat10', 'COUNT_cat11', 'COUNT_cat12', 'COUNT_cat13', 'COUNT_cat14', 'COUNT_cat15', 'COUNT_cat16', 'COUNT_cat17', 'COUNT_cat18', 'COUNT_cat19', 'COUNT_cat20', 'COUNT_cat21', 'COUNT_cat22', 'COUNT_cat23', 'COUNT_cat24', 'COUNT_cat25', 'COUNT_cat26', 'COUNT_cat27', 'COUNT_cat28', 'COUNT_cat29', 'COUNT_cat30', 'COUNT_cat31', 'COUNT_cat32', 'COUNT_cat33', 'COUNT_cat34', 'COUNT_cat35', 'COUNT_cat36', 'COUNT_cat37', 'COUNT_cat38', 'COUNT_cat39', 'COUNT_cat40', 'COUNT_cat41', 'COUNT_cat42', 'COUNT_cat43', 'COUNT_cat44', 'COUNT_cat45', 'COUNT_cat46', 'COUNT_cat47', 'COUNT_cat48', 'COUNT_cat49', 'COUNT_cat50', 'COUNT_cat51', 'COUNT_cat52', 'COUNT_cat53', 'COUNT_cat54', 'COUNT_cat55', 'COUNT_cat56', 'COUNT_cat57', 'COUNT_cat58', 'COUNT_cat59', 'COUNT_cat60', 'COUNT_cat61', 'COUNT_cat62', 'COUNT_cat63', 'COUNT_cat64', 'COUNT_cat65', 'COUNT_cat66', 'COUNT_cat67', 'COUNT_cat68', 'COUNT_cat69', 'COUNT_cat70', 'COUNT_cat71', 'COUNT_cat72', 'COUNT_cat73', 'COUNT_cat74', 'COUNT_cat75', 'COUNT_cat76', 'COUNT_cat77', 'COUNT_cat78', 'COUNT_cat79', 'COUNT_cat80', 'COUNT_cat81', 'COUNT_cat82', 'COUNT_cat83', 'COUNT_cat84', 'COUNT_cat85', 'COUNT_cat86', 'COUNT_cat87', 'COUNT_cat88', 'COUNT_cat89', 'COUNT_cat90', 'COUNT_cat91', 'COUNT_cat92', 'COUNT_cat93', 'COUNT_cat94', 'COUNT_cat95', 'COUNT_cat96', 'COUNT_cat97', 'COUNT_cat98', 'COUNT_cat99', 'COUNT_cat100', 'COUNT_cat101', 'COUNT_cat102', 'COUNT_cat103', 'COUNT_cat104', 'COUNT_cat105', 'COUNT_cat106', 'COUNT_cat107', 'COUNT_cat108', 'COUNT_cat109', 'COUNT_cat110', 'COUNT_cat111', 'COUNT_cat112', 'COUNT_cat113', 'COUNT_cat114', 'COUNT_cat115', 'COUNT_cat116', 'cat89__id__rank', 'cat89__cont1__rank', 'cat89__cont2__rank', 'cat89__cont3__rank', 'cat89__cont4__rank', 'cat89__cont5__rank', 'cat89__cont6__rank', 'cat89__cont7__rank', 'cat89__cont8__rank', 'cat89__cont9__rank', 'cat89__cont10__rank', 'cat89__cont11__rank', 'cat89__cont12__rank', 'cat89__cont13__rank', 'cat89__cont14__rank', 'cat90__id__rank', 'cat90__cont1__rank', 'cat90__cont2__rank', 'cat90__cont3__rank', 'cat90__cont4__rank', 'cat90__cont5__rank', 'cat90__cont6__rank', 'cat90__cont7__rank', 'cat90__cont8__rank', 'cat90__cont9__rank', 'cat90__cont10__rank', 'cat90__cont11__rank', 'cat90__cont12__rank', 'cat90__cont13__rank', 'cat90__cont14__rank', 'cat91__id__rank', 'cat91__cont1__rank', 'cat91__cont2__rank', 'cat91__cont3__rank', 'cat91__cont4__rank', 'cat91__cont5__rank', 'cat91__cont6__rank', 'cat91__cont7__rank', 'cat91__cont8__rank', 'cat91__cont9__rank', 'cat91__cont10__rank', 'cat91__cont11__rank', 'cat91__cont12__rank', 'cat91__cont13__rank', 'cat91__cont14__rank', 'cat92__id__rank', 'cat92__cont1__rank', 'cat92__cont2__rank', 'cat92__cont3__rank', 'cat92__cont4__rank', 'cat92__cont5__rank', 'cat92__cont6__rank', 'cat92__cont7__rank', 'cat92__cont8__rank', 'cat92__cont9__rank', 'cat92__cont10__rank', 'cat92__cont11__rank', 'cat92__cont12__rank', 'cat92__cont13__rank', 'cat92__cont14__rank', 'cat93__id__rank', 'cat93__cont1__rank', 'cat93__cont2__rank', 'cat93__cont3__rank', 'cat93__cont4__rank', 'cat93__cont5__rank', 'cat93__cont6__rank', 'cat93__cont7__rank', 'cat93__cont8__rank', 'cat93__cont9__rank', 'cat93__cont10__rank', 'cat93__cont11__rank', 'cat93__cont12__rank', 'cat93__cont13__rank', 'cat93__cont14__rank', 'cat94__id__rank', 'cat94__cont1__rank', 'cat94__cont2__rank', 'cat94__cont3__rank', 'cat94__cont4__rank', 'cat94__cont5__rank', 'cat94__cont6__rank', 'cat94__cont7__rank', 'cat94__cont8__rank', 'cat94__cont9__rank', 'cat94__cont10__rank', 'cat94__cont11__rank', 'cat94__cont12__rank', 'cat94__cont13__rank', 'cat94__cont14__rank', 'cat95__id__rank', 'cat95__cont1__rank', 'cat95__cont2__rank', 'cat95__cont3__rank', 'cat95__cont4__rank', 'cat95__cont5__rank', 'cat95__cont6__rank', 'cat95__cont7__rank', 'cat95__cont8__rank', 'cat95__cont9__rank', 'cat95__cont10__rank', 'cat95__cont11__rank', 'cat95__cont12__rank', 'cat95__cont13__rank', 'cat95__cont14__rank', 'cat96__id__rank', 'cat96__cont1__rank', 'cat96__cont2__rank', 'cat96__cont3__rank', 'cat96__cont4__rank', 'cat96__cont5__rank', 'cat96__cont6__rank', 'cat96__cont7__rank', 'cat96__cont8__rank', 'cat96__cont9__rank', 'cat96__cont10__rank', 'cat96__cont11__rank', 'cat96__cont12__rank', 'cat96__cont13__rank', 'cat96__cont14__rank', 'cat97__id__rank', 'cat97__cont1__rank', 'cat97__cont2__rank', 'cat97__cont3__rank', 'cat97__cont4__rank', 'cat97__cont5__rank', 'cat97__cont6__rank', 'cat97__cont7__rank', 'cat97__cont8__rank', 'cat97__cont9__rank', 'cat97__cont10__rank', 'cat97__cont11__rank', 'cat97__cont12__rank', 'cat97__cont13__rank', 'cat97__cont14__rank', 'cat98__id__rank', 'cat98__cont1__rank', 'cat98__cont2__rank', 'cat98__cont3__rank', 'cat98__cont4__rank', 'cat98__cont5__rank', 'cat98__cont6__rank', 'cat98__cont7__rank', 'cat98__cont8__rank', 'cat98__cont9__rank', 'cat98__cont10__rank', 'cat98__cont11__rank', 'cat98__cont12__rank', 'cat98__cont13__rank', 'cat98__cont14__rank', 'cat99__id__rank', 'cat99__cont1__rank', 'cat99__cont2__rank', 'cat99__cont3__rank', 'cat99__cont4__rank', 'cat99__cont5__rank', 'cat99__cont6__rank', 'cat99__cont7__rank', 'cat99__cont8__rank', 'cat99__cont9__rank', 'cat99__cont10__rank', 'cat99__cont11__rank', 'cat99__cont12__rank', 'cat99__cont13__rank', 'cat99__cont14__rank', 'cat100__id__rank', 'cat100__cont1__rank', 'cat100__cont2__rank', 'cat100__cont3__rank', 'cat100__cont4__rank', 'cat100__cont5__rank', 'cat100__cont6__rank', 'cat100__cont7__rank', 'cat100__cont8__rank', 'cat100__cont9__rank', 'cat100__cont10__rank', 'cat100__cont11__rank', 'cat100__cont12__rank', 'cat100__cont13__rank', 'cat100__cont14__rank', 'cat101__id__rank', 'cat101__cont1__rank', 'cat101__cont2__rank', 'cat101__cont3__rank', 'cat101__cont4__rank', 'cat101__cont5__rank', 'cat101__cont6__rank', 'cat101__cont7__rank', 'cat101__cont8__rank', 'cat101__cont9__rank', 'cat101__cont10__rank', 'cat101__cont11__rank', 'cat101__cont12__rank', 'cat101__cont13__rank', 'cat101__cont14__rank', 'cat102__id__rank', 'cat102__cont1__rank', 'cat102__cont2__rank', 'cat102__cont3__rank', 'cat102__cont4__rank', 'cat102__cont5__rank', 'cat102__cont6__rank', 'cat102__cont7__rank', 'cat102__cont8__rank', 'cat102__cont9__rank', 'cat102__cont10__rank', 'cat102__cont11__rank', 'cat102__cont12__rank', 'cat102__cont13__rank', 'cat102__cont14__rank', 'cat103__id__rank', 'cat103__cont1__rank', 'cat103__cont2__rank', 'cat103__cont3__rank', 'cat103__cont4__rank', 'cat103__cont5__rank', 'cat103__cont6__rank', 'cat103__cont7__rank', 'cat103__cont8__rank', 'cat103__cont9__rank', 'cat103__cont10__rank', 'cat103__cont11__rank', 'cat103__cont12__rank', 'cat103__cont13__rank', 'cat103__cont14__rank', 'cat104__id__rank', 'cat104__cont1__rank', 'cat104__cont2__rank', 'cat104__cont3__rank', 'cat104__cont4__rank', 'cat104__cont5__rank', 'cat104__cont6__rank', 'cat104__cont7__rank', 'cat104__cont8__rank', 'cat104__cont9__rank', 'cat104__cont10__rank', 'cat104__cont11__rank', 'cat104__cont12__rank', 'cat104__cont13__rank', 'cat104__cont14__rank', 'cat105__id__rank', 'cat105__cont1__rank', 'cat105__cont2__rank', 'cat105__cont3__rank', 'cat105__cont4__rank', 'cat105__cont5__rank', 'cat105__cont6__rank', 'cat105__cont7__rank', 'cat105__cont8__rank', 'cat105__cont9__rank', 'cat105__cont10__rank', 'cat105__cont11__rank', 'cat105__cont12__rank', 'cat105__cont13__rank', 'cat105__cont14__rank', 'cat106__id__rank', 'cat106__cont1__rank', 'cat106__cont2__rank', 'cat106__cont3__rank', 'cat106__cont4__rank', 'cat106__cont5__rank', 'cat106__cont6__rank', 'cat106__cont7__rank', 'cat106__cont8__rank', 'cat106__cont9__rank', 'cat106__cont10__rank', 'cat106__cont11__rank', 'cat106__cont12__rank', 'cat106__cont13__rank', 'cat106__cont14__rank', 'cat107__id__rank', 'cat107__cont1__rank', 'cat107__cont2__rank', 'cat107__cont3__rank', 'cat107__cont4__rank', 'cat107__cont5__rank', 'cat107__cont6__rank', 'cat107__cont7__rank', 'cat107__cont8__rank', 'cat107__cont9__rank', 'cat107__cont10__rank', 'cat107__cont11__rank', 'cat107__cont12__rank', 'cat107__cont13__rank', 'cat107__cont14__rank', 'cat108__id__rank', 'cat108__cont1__rank', 'cat108__cont2__rank', 'cat108__cont3__rank', 'cat108__cont4__rank', 'cat108__cont5__rank', 'cat108__cont6__rank', 'cat108__cont7__rank', 'cat108__cont8__rank', 'cat108__cont9__rank', 'cat108__cont10__rank', 'cat108__cont11__rank', 'cat108__cont12__rank', 'cat108__cont13__rank', 'cat108__cont14__rank', 'cat109__id__rank', 'cat109__cont1__rank', 'cat109__cont2__rank', 'cat109__cont3__rank', 'cat109__cont4__rank', 'cat109__cont5__rank', 'cat109__cont6__rank', 'cat109__cont7__rank', 'cat109__cont8__rank', 'cat109__cont9__rank', 'cat109__cont10__rank', 'cat109__cont11__rank', 'cat109__cont12__rank', 'cat109__cont13__rank', 'cat109__cont14__rank', 'cat110__id__rank', 'cat110__cont1__rank', 'cat110__cont2__rank', 'cat110__cont3__rank', 'cat110__cont4__rank', 'cat110__cont5__rank', 'cat110__cont6__rank', 'cat110__cont7__rank', 'cat110__cont8__rank', 'cat110__cont9__rank', 'cat110__cont10__rank', 'cat110__cont11__rank', 'cat110__cont12__rank', 'cat110__cont13__rank', 'cat110__cont14__rank', 'cat111__id__rank', 'cat111__cont1__rank', 'cat111__cont2__rank', 'cat111__cont3__rank', 'cat111__cont4__rank', 'cat111__cont5__rank', 'cat111__cont6__rank', 'cat111__cont7__rank', 'cat111__cont8__rank', 'cat111__cont9__rank', 'cat111__cont10__rank', 'cat111__cont11__rank', 'cat111__cont12__rank', 'cat111__cont13__rank', 'cat111__cont14__rank', 'cat112__id__rank', 'cat112__cont1__rank', 'cat112__cont2__rank', 'cat112__cont3__rank', 'cat112__cont4__rank', 'cat112__cont5__rank', 'cat112__cont6__rank', 'cat112__cont7__rank', 'cat112__cont8__rank', 'cat112__cont9__rank', 'cat112__cont10__rank', 'cat112__cont11__rank', 'cat112__cont12__rank', 'cat112__cont13__rank', 'cat112__cont14__rank', 'cat113__id__rank', 'cat113__cont1__rank', 'cat113__cont2__rank', 'cat113__cont3__rank', 'cat113__cont4__rank', 'cat113__cont5__rank', 'cat113__cont6__rank', 'cat113__cont7__rank', 'cat113__cont8__rank', 'cat113__cont9__rank', 'cat113__cont10__rank', 'cat113__cont11__rank', 'cat113__cont12__rank', 'cat113__cont13__rank', 'cat113__cont14__rank', 'cat114__id__rank', 'cat114__cont1__rank', 'cat114__cont2__rank', 'cat114__cont3__rank', 'cat114__cont4__rank', 'cat114__cont5__rank', 'cat114__cont6__rank', 'cat114__cont7__rank', 'cat114__cont8__rank', 'cat114__cont9__rank', 'cat114__cont10__rank', 'cat114__cont11__rank', 'cat114__cont12__rank', 'cat114__cont13__rank', 'cat114__cont14__rank', 'cat115__id__rank', 'cat115__cont1__rank', 'cat115__cont2__rank', 'cat115__cont3__rank', 'cat115__cont4__rank', 'cat115__cont5__rank', 'cat115__cont6__rank', 'cat115__cont7__rank', 'cat115__cont8__rank', 'cat115__cont9__rank', 'cat115__cont10__rank', 'cat115__cont11__rank', 'cat115__cont12__rank', 'cat115__cont13__rank', 'cat115__cont14__rank', 'cat116__id__rank', 'cat116__cont1__rank', 'cat116__cont2__rank', 'cat116__cont3__rank', 'cat116__cont4__rank', 'cat116__cont5__rank', 'cat116__cont6__rank', 'cat116__cont7__rank', 'cat116__cont8__rank', 'cat116__cont9__rank', 'cat116__cont10__rank', 'cat116__cont11__rank', 'cat116__cont12__rank', 'cat116__cont13__rank', 'cat116__cont14__rank']\n",
      "   INFO ->  start training model\n",
      "   INFO ->  (188318, 666)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on fold 1\n",
      "Training until validation scores don't improve for 150 rounds\n",
      "[100]\ttraining's l1: 0.518239\tvalid_1's l1: 0.517726\n",
      "[200]\ttraining's l1: 0.474579\tvalid_1's l1: 0.475565\n",
      "[300]\ttraining's l1: 0.45302\tvalid_1's l1: 0.455052\n",
      "[400]\ttraining's l1: 0.440509\tvalid_1's l1: 0.443131\n",
      "[500]\ttraining's l1: 0.432359\tvalid_1's l1: 0.435507\n",
      "[600]\ttraining's l1: 0.426983\tvalid_1's l1: 0.430738\n",
      "[700]\ttraining's l1: 0.423201\tvalid_1's l1: 0.427555\n",
      "[800]\ttraining's l1: 0.420284\tvalid_1's l1: 0.425339\n",
      "[900]\ttraining's l1: 0.41798\tvalid_1's l1: 0.423787\n",
      "[1000]\ttraining's l1: 0.416056\tvalid_1's l1: 0.422569\n",
      "[1100]\ttraining's l1: 0.414427\tvalid_1's l1: 0.42161\n",
      "[1200]\ttraining's l1: 0.412974\tvalid_1's l1: 0.420835\n",
      "[1300]\ttraining's l1: 0.411624\tvalid_1's l1: 0.420144\n",
      "[1400]\ttraining's l1: 0.41042\tvalid_1's l1: 0.419601\n",
      "[1500]\ttraining's l1: 0.409305\tvalid_1's l1: 0.41915\n",
      "[1600]\ttraining's l1: 0.408287\tvalid_1's l1: 0.418773\n",
      "[1700]\ttraining's l1: 0.407306\tvalid_1's l1: 0.418419\n",
      "[1800]\ttraining's l1: 0.406429\tvalid_1's l1: 0.418134\n",
      "[1900]\ttraining's l1: 0.405515\tvalid_1's l1: 0.417831\n",
      "[2000]\ttraining's l1: 0.404724\tvalid_1's l1: 0.417598\n",
      "[2100]\ttraining's l1: 0.40391\tvalid_1's l1: 0.417358\n",
      "[2200]\ttraining's l1: 0.403127\tvalid_1's l1: 0.417132\n",
      "[2300]\ttraining's l1: 0.402387\tvalid_1's l1: 0.416954\n",
      "[2400]\ttraining's l1: 0.401617\tvalid_1's l1: 0.41678\n",
      "[2500]\ttraining's l1: 0.400862\tvalid_1's l1: 0.416595\n",
      "[2600]\ttraining's l1: 0.400147\tvalid_1's l1: 0.416453\n",
      "[2700]\ttraining's l1: 0.399461\tvalid_1's l1: 0.416317\n",
      "[2800]\ttraining's l1: 0.398745\tvalid_1's l1: 0.416181\n",
      "[2900]\ttraining's l1: 0.398051\tvalid_1's l1: 0.416043\n",
      "[3000]\ttraining's l1: 0.397348\tvalid_1's l1: 0.415936\n",
      "[3100]\ttraining's l1: 0.396694\tvalid_1's l1: 0.415817\n",
      "[3200]\ttraining's l1: 0.396055\tvalid_1's l1: 0.41571\n",
      "[3300]\ttraining's l1: 0.395397\tvalid_1's l1: 0.415635\n",
      "[3400]\ttraining's l1: 0.394746\tvalid_1's l1: 0.415543\n",
      "[3500]\ttraining's l1: 0.394095\tvalid_1's l1: 0.415469\n",
      "[3600]\ttraining's l1: 0.393461\tvalid_1's l1: 0.415378\n",
      "[3700]\ttraining's l1: 0.392851\tvalid_1's l1: 0.415295\n",
      "[3800]\ttraining's l1: 0.392204\tvalid_1's l1: 0.415252\n",
      "[3900]\ttraining's l1: 0.391574\tvalid_1's l1: 0.415179\n",
      "[4000]\ttraining's l1: 0.390964\tvalid_1's l1: 0.415111\n",
      "[4100]\ttraining's l1: 0.390335\tvalid_1's l1: 0.415047\n",
      "[4200]\ttraining's l1: 0.389736\tvalid_1's l1: 0.414994\n",
      "[4300]\ttraining's l1: 0.389132\tvalid_1's l1: 0.414948\n",
      "[4400]\ttraining's l1: 0.388524\tvalid_1's l1: 0.4149\n",
      "[4500]\ttraining's l1: 0.387915\tvalid_1's l1: 0.414869\n",
      "[4600]\ttraining's l1: 0.387339\tvalid_1's l1: 0.414827\n",
      "[4700]\ttraining's l1: 0.386742\tvalid_1's l1: 0.414797\n",
      "[4800]\ttraining's l1: 0.386194\tvalid_1's l1: 0.414784\n",
      "[4900]\ttraining's l1: 0.385638\tvalid_1's l1: 0.414761\n",
      "[5000]\ttraining's l1: 0.385067\tvalid_1's l1: 0.41473\n",
      "[5100]\ttraining's l1: 0.38449\tvalid_1's l1: 0.414681\n",
      "[5200]\ttraining's l1: 0.38391\tvalid_1's l1: 0.414668\n",
      "[5300]\ttraining's l1: 0.383355\tvalid_1's l1: 0.414653\n",
      "[5400]\ttraining's l1: 0.382793\tvalid_1's l1: 0.414636\n",
      "[5500]\ttraining's l1: 0.382261\tvalid_1's l1: 0.414619\n",
      "[5600]\ttraining's l1: 0.38168\tvalid_1's l1: 0.414607\n",
      "[5700]\ttraining's l1: 0.381133\tvalid_1's l1: 0.414598\n",
      "[5800]\ttraining's l1: 0.380586\tvalid_1's l1: 0.414579\n",
      "[5900]\ttraining's l1: 0.380048\tvalid_1's l1: 0.41456\n",
      "[6000]\ttraining's l1: 0.37953\tvalid_1's l1: 0.41456\n",
      "[6100]\ttraining's l1: 0.378993\tvalid_1's l1: 0.414539\n",
      "[6200]\ttraining's l1: 0.378459\tvalid_1's l1: 0.414515\n",
      "[6300]\ttraining's l1: 0.377912\tvalid_1's l1: 0.414508\n",
      "[6400]\ttraining's l1: 0.377393\tvalid_1's l1: 0.414498\n",
      "[6500]\ttraining's l1: 0.376876\tvalid_1's l1: 0.414486\n",
      "[6600]\ttraining's l1: 0.376334\tvalid_1's l1: 0.414471\n",
      "[6700]\ttraining's l1: 0.375797\tvalid_1's l1: 0.414456\n",
      "[6800]\ttraining's l1: 0.375272\tvalid_1's l1: 0.414442\n",
      "[6900]\ttraining's l1: 0.37475\tvalid_1's l1: 0.414439\n",
      "[7000]\ttraining's l1: 0.374202\tvalid_1's l1: 0.414443\n",
      "Early stopping, best iteration is:\n",
      "[6859]\ttraining's l1: 0.37496\tvalid_1's l1: 0.414433\n",
      "MSE: 3620887.7316554645\n",
      "Fold 1 finished in 1:47:23.325256\n",
      "Training on fold 2\n",
      "Training until validation scores don't improve for 150 rounds\n",
      "[100]\ttraining's l1: 0.517829\tvalid_1's l1: 0.518326\n",
      "[200]\ttraining's l1: 0.474172\tvalid_1's l1: 0.476393\n",
      "[300]\ttraining's l1: 0.452708\tvalid_1's l1: 0.456198\n",
      "[400]\ttraining's l1: 0.440066\tvalid_1's l1: 0.444437\n",
      "[500]\ttraining's l1: 0.431894\tvalid_1's l1: 0.437106\n",
      "[600]\ttraining's l1: 0.426541\tvalid_1's l1: 0.432589\n",
      "[700]\ttraining's l1: 0.422803\tvalid_1's l1: 0.429677\n",
      "[800]\ttraining's l1: 0.419908\tvalid_1's l1: 0.427568\n",
      "[900]\ttraining's l1: 0.417607\tvalid_1's l1: 0.426023\n",
      "[1000]\ttraining's l1: 0.415659\tvalid_1's l1: 0.424749\n",
      "[1100]\ttraining's l1: 0.41405\tvalid_1's l1: 0.423771\n",
      "[1200]\ttraining's l1: 0.412544\tvalid_1's l1: 0.42288\n",
      "[1300]\ttraining's l1: 0.411153\tvalid_1's l1: 0.422138\n",
      "[1400]\ttraining's l1: 0.409976\tvalid_1's l1: 0.42155\n",
      "[1500]\ttraining's l1: 0.408878\tvalid_1's l1: 0.421088\n",
      "[1600]\ttraining's l1: 0.407804\tvalid_1's l1: 0.420624\n",
      "[1700]\ttraining's l1: 0.4068\tvalid_1's l1: 0.420242\n",
      "[1800]\ttraining's l1: 0.405845\tvalid_1's l1: 0.41994\n",
      "[1900]\ttraining's l1: 0.404927\tvalid_1's l1: 0.41969\n",
      "[2000]\ttraining's l1: 0.404074\tvalid_1's l1: 0.419454\n",
      "[2100]\ttraining's l1: 0.403248\tvalid_1's l1: 0.41923\n",
      "[2200]\ttraining's l1: 0.402421\tvalid_1's l1: 0.419043\n",
      "[2300]\ttraining's l1: 0.401649\tvalid_1's l1: 0.41885\n",
      "[2400]\ttraining's l1: 0.400894\tvalid_1's l1: 0.418703\n",
      "[2500]\ttraining's l1: 0.400113\tvalid_1's l1: 0.418537\n",
      "[2600]\ttraining's l1: 0.399394\tvalid_1's l1: 0.418401\n",
      "[2700]\ttraining's l1: 0.398688\tvalid_1's l1: 0.418279\n",
      "[2800]\ttraining's l1: 0.39798\tvalid_1's l1: 0.418171\n",
      "[2900]\ttraining's l1: 0.397291\tvalid_1's l1: 0.418056\n",
      "[3000]\ttraining's l1: 0.396592\tvalid_1's l1: 0.417943\n",
      "[3100]\ttraining's l1: 0.395913\tvalid_1's l1: 0.41786\n",
      "[3200]\ttraining's l1: 0.395244\tvalid_1's l1: 0.417796\n",
      "[3300]\ttraining's l1: 0.3946\tvalid_1's l1: 0.417731\n",
      "[3400]\ttraining's l1: 0.393952\tvalid_1's l1: 0.417665\n",
      "[3500]\ttraining's l1: 0.393322\tvalid_1's l1: 0.417599\n",
      "[3600]\ttraining's l1: 0.392661\tvalid_1's l1: 0.417556\n",
      "[3700]\ttraining's l1: 0.392035\tvalid_1's l1: 0.417497\n",
      "[3800]\ttraining's l1: 0.391431\tvalid_1's l1: 0.417451\n",
      "[3900]\ttraining's l1: 0.390786\tvalid_1's l1: 0.417401\n",
      "[4000]\ttraining's l1: 0.390182\tvalid_1's l1: 0.417377\n",
      "[4100]\ttraining's l1: 0.389558\tvalid_1's l1: 0.417329\n",
      "[4200]\ttraining's l1: 0.388971\tvalid_1's l1: 0.417303\n",
      "[4300]\ttraining's l1: 0.388358\tvalid_1's l1: 0.417267\n",
      "[4400]\ttraining's l1: 0.387788\tvalid_1's l1: 0.417209\n",
      "[4500]\ttraining's l1: 0.387202\tvalid_1's l1: 0.417194\n",
      "[4600]\ttraining's l1: 0.386627\tvalid_1's l1: 0.417168\n",
      "[4700]\ttraining's l1: 0.386054\tvalid_1's l1: 0.41713\n",
      "[4800]\ttraining's l1: 0.385474\tvalid_1's l1: 0.41711\n",
      "[4900]\ttraining's l1: 0.384871\tvalid_1's l1: 0.417093\n",
      "[5000]\ttraining's l1: 0.384324\tvalid_1's l1: 0.417076\n",
      "[5100]\ttraining's l1: 0.383748\tvalid_1's l1: 0.417062\n",
      "[5200]\ttraining's l1: 0.383179\tvalid_1's l1: 0.417037\n",
      "[5300]\ttraining's l1: 0.382615\tvalid_1's l1: 0.417014\n",
      "[5400]\ttraining's l1: 0.382045\tvalid_1's l1: 0.417005\n",
      "[5500]\ttraining's l1: 0.381473\tvalid_1's l1: 0.416993\n",
      "[5600]\ttraining's l1: 0.380941\tvalid_1's l1: 0.417001\n",
      "[5700]\ttraining's l1: 0.38037\tvalid_1's l1: 0.416961\n",
      "[5800]\ttraining's l1: 0.379815\tvalid_1's l1: 0.41695\n",
      "[5900]\ttraining's l1: 0.379271\tvalid_1's l1: 0.416956\n",
      "Early stopping, best iteration is:\n",
      "[5833]\ttraining's l1: 0.379636\tvalid_1's l1: 0.416947\n",
      "MSE: 3917502.4603431816\n",
      "Fold 2 finished in 1:42:27.540137\n",
      "Training on fold 3\n",
      "Training until validation scores don't improve for 150 rounds\n",
      "[100]\ttraining's l1: 0.517505\tvalid_1's l1: 0.520404\n",
      "[200]\ttraining's l1: 0.474055\tvalid_1's l1: 0.476933\n",
      "[300]\ttraining's l1: 0.452874\tvalid_1's l1: 0.456321\n",
      "[400]\ttraining's l1: 0.440367\tvalid_1's l1: 0.444628\n",
      "[500]\ttraining's l1: 0.43228\tvalid_1's l1: 0.437281\n",
      "[600]\ttraining's l1: 0.426904\tvalid_1's l1: 0.432619\n",
      "[700]\ttraining's l1: 0.423161\tvalid_1's l1: 0.429615\n",
      "[800]\ttraining's l1: 0.420283\tvalid_1's l1: 0.427462\n",
      "[900]\ttraining's l1: 0.417894\tvalid_1's l1: 0.425779\n",
      "[1000]\ttraining's l1: 0.415877\tvalid_1's l1: 0.424468\n",
      "[1100]\ttraining's l1: 0.414165\tvalid_1's l1: 0.42342\n",
      "[1200]\ttraining's l1: 0.412579\tvalid_1's l1: 0.42246\n",
      "[1300]\ttraining's l1: 0.411233\tvalid_1's l1: 0.421735\n",
      "[1400]\ttraining's l1: 0.409992\tvalid_1's l1: 0.421122\n",
      "[1500]\ttraining's l1: 0.408873\tvalid_1's l1: 0.420604\n",
      "[1600]\ttraining's l1: 0.407821\tvalid_1's l1: 0.420181\n",
      "[1700]\ttraining's l1: 0.406804\tvalid_1's l1: 0.419771\n",
      "[1800]\ttraining's l1: 0.405865\tvalid_1's l1: 0.419413\n",
      "[1900]\ttraining's l1: 0.404938\tvalid_1's l1: 0.4191\n",
      "[2000]\ttraining's l1: 0.404048\tvalid_1's l1: 0.418806\n",
      "[2100]\ttraining's l1: 0.403209\tvalid_1's l1: 0.418564\n",
      "[2200]\ttraining's l1: 0.402389\tvalid_1's l1: 0.418369\n",
      "[2300]\ttraining's l1: 0.401564\tvalid_1's l1: 0.418203\n",
      "[2400]\ttraining's l1: 0.400794\tvalid_1's l1: 0.41806\n",
      "[2500]\ttraining's l1: 0.400044\tvalid_1's l1: 0.417914\n",
      "[2600]\ttraining's l1: 0.399312\tvalid_1's l1: 0.417761\n",
      "[2700]\ttraining's l1: 0.398607\tvalid_1's l1: 0.417621\n",
      "[2800]\ttraining's l1: 0.397851\tvalid_1's l1: 0.417486\n",
      "[2900]\ttraining's l1: 0.397193\tvalid_1's l1: 0.417387\n",
      "[3000]\ttraining's l1: 0.396516\tvalid_1's l1: 0.417276\n",
      "[3100]\ttraining's l1: 0.395837\tvalid_1's l1: 0.417186\n",
      "[3200]\ttraining's l1: 0.395146\tvalid_1's l1: 0.417098\n",
      "[3300]\ttraining's l1: 0.394472\tvalid_1's l1: 0.417025\n",
      "[3400]\ttraining's l1: 0.393833\tvalid_1's l1: 0.416951\n",
      "[3500]\ttraining's l1: 0.393184\tvalid_1's l1: 0.4169\n",
      "[3600]\ttraining's l1: 0.392546\tvalid_1's l1: 0.416823\n",
      "[3700]\ttraining's l1: 0.391925\tvalid_1's l1: 0.416761\n",
      "[3800]\ttraining's l1: 0.39131\tvalid_1's l1: 0.416697\n",
      "[3900]\ttraining's l1: 0.39071\tvalid_1's l1: 0.416649\n",
      "[4000]\ttraining's l1: 0.390096\tvalid_1's l1: 0.416573\n",
      "[4100]\ttraining's l1: 0.389515\tvalid_1's l1: 0.41651\n",
      "[4200]\ttraining's l1: 0.388903\tvalid_1's l1: 0.416474\n",
      "[4300]\ttraining's l1: 0.388291\tvalid_1's l1: 0.41643\n",
      "[4400]\ttraining's l1: 0.387696\tvalid_1's l1: 0.416404\n",
      "[4500]\ttraining's l1: 0.387124\tvalid_1's l1: 0.416369\n",
      "[4600]\ttraining's l1: 0.386513\tvalid_1's l1: 0.416337\n",
      "[4700]\ttraining's l1: 0.385936\tvalid_1's l1: 0.416298\n",
      "[4800]\ttraining's l1: 0.385353\tvalid_1's l1: 0.416287\n",
      "[4900]\ttraining's l1: 0.384776\tvalid_1's l1: 0.416276\n",
      "[5000]\ttraining's l1: 0.384231\tvalid_1's l1: 0.416245\n",
      "[5100]\ttraining's l1: 0.383648\tvalid_1's l1: 0.41621\n",
      "[5200]\ttraining's l1: 0.383083\tvalid_1's l1: 0.416206\n",
      "[5300]\ttraining's l1: 0.382542\tvalid_1's l1: 0.416191\n",
      "[5400]\ttraining's l1: 0.382003\tvalid_1's l1: 0.41618\n",
      "[5500]\ttraining's l1: 0.381455\tvalid_1's l1: 0.416169\n",
      "[5600]\ttraining's l1: 0.380922\tvalid_1's l1: 0.416146\n",
      "[5700]\ttraining's l1: 0.380361\tvalid_1's l1: 0.41612\n",
      "[5800]\ttraining's l1: 0.379814\tvalid_1's l1: 0.41611\n",
      "[5900]\ttraining's l1: 0.379272\tvalid_1's l1: 0.416121\n",
      "[6000]\ttraining's l1: 0.378735\tvalid_1's l1: 0.416107\n",
      "[6100]\ttraining's l1: 0.378205\tvalid_1's l1: 0.416108\n",
      "[6200]\ttraining's l1: 0.377642\tvalid_1's l1: 0.416074\n",
      "[6300]\ttraining's l1: 0.377109\tvalid_1's l1: 0.41606\n",
      "[6400]\ttraining's l1: 0.376574\tvalid_1's l1: 0.416051\n",
      "[6500]\ttraining's l1: 0.376048\tvalid_1's l1: 0.416068\n",
      "Early stopping, best iteration is:\n",
      "[6364]\ttraining's l1: 0.37676\tvalid_1's l1: 0.416041\n",
      "MSE: 3799604.7103570746\n",
      "Fold 3 finished in 1:36:24.493696\n",
      "Training on fold 4\n",
      "Training until validation scores don't improve for 150 rounds\n",
      "[100]\ttraining's l1: 0.51831\tvalid_1's l1: 0.518006\n",
      "[200]\ttraining's l1: 0.474625\tvalid_1's l1: 0.474943\n",
      "[300]\ttraining's l1: 0.453262\tvalid_1's l1: 0.454516\n",
      "[400]\ttraining's l1: 0.440508\tvalid_1's l1: 0.442645\n",
      "[500]\ttraining's l1: 0.432634\tvalid_1's l1: 0.435472\n",
      "[600]\ttraining's l1: 0.427416\tvalid_1's l1: 0.430922\n",
      "[700]\ttraining's l1: 0.423741\tvalid_1's l1: 0.427933\n",
      "[800]\ttraining's l1: 0.420822\tvalid_1's l1: 0.425635\n",
      "[900]\ttraining's l1: 0.418505\tvalid_1's l1: 0.423978\n",
      "[1000]\ttraining's l1: 0.416598\tvalid_1's l1: 0.42266\n",
      "[1100]\ttraining's l1: 0.414926\tvalid_1's l1: 0.421595\n",
      "[1200]\ttraining's l1: 0.413459\tvalid_1's l1: 0.420718\n",
      "[1300]\ttraining's l1: 0.412155\tvalid_1's l1: 0.419975\n",
      "[1400]\ttraining's l1: 0.410978\tvalid_1's l1: 0.419342\n",
      "[1500]\ttraining's l1: 0.409843\tvalid_1's l1: 0.418803\n",
      "[1600]\ttraining's l1: 0.40877\tvalid_1's l1: 0.41833\n",
      "[1700]\ttraining's l1: 0.40779\tvalid_1's l1: 0.417953\n",
      "[1800]\ttraining's l1: 0.406867\tvalid_1's l1: 0.417631\n",
      "[1900]\ttraining's l1: 0.405975\tvalid_1's l1: 0.417334\n",
      "[2000]\ttraining's l1: 0.405119\tvalid_1's l1: 0.417066\n",
      "[2100]\ttraining's l1: 0.404265\tvalid_1's l1: 0.416801\n",
      "[2200]\ttraining's l1: 0.40344\tvalid_1's l1: 0.416578\n",
      "[2300]\ttraining's l1: 0.402658\tvalid_1's l1: 0.416396\n",
      "[2400]\ttraining's l1: 0.401858\tvalid_1's l1: 0.416227\n",
      "[2500]\ttraining's l1: 0.401092\tvalid_1's l1: 0.416049\n",
      "[2600]\ttraining's l1: 0.400383\tvalid_1's l1: 0.415909\n",
      "[2700]\ttraining's l1: 0.399648\tvalid_1's l1: 0.415753\n",
      "[2800]\ttraining's l1: 0.398926\tvalid_1's l1: 0.415638\n",
      "[2900]\ttraining's l1: 0.398197\tvalid_1's l1: 0.415508\n",
      "[3000]\ttraining's l1: 0.397471\tvalid_1's l1: 0.415368\n",
      "[3100]\ttraining's l1: 0.39679\tvalid_1's l1: 0.415291\n",
      "[3200]\ttraining's l1: 0.396123\tvalid_1's l1: 0.415202\n",
      "[3300]\ttraining's l1: 0.395457\tvalid_1's l1: 0.415121\n",
      "[3400]\ttraining's l1: 0.394789\tvalid_1's l1: 0.415032\n",
      "[3500]\ttraining's l1: 0.394122\tvalid_1's l1: 0.414959\n",
      "[3600]\ttraining's l1: 0.393465\tvalid_1's l1: 0.414893\n",
      "[3700]\ttraining's l1: 0.392823\tvalid_1's l1: 0.414837\n",
      "[3800]\ttraining's l1: 0.392167\tvalid_1's l1: 0.414748\n",
      "[3900]\ttraining's l1: 0.391569\tvalid_1's l1: 0.414713\n",
      "[4000]\ttraining's l1: 0.390932\tvalid_1's l1: 0.414655\n",
      "[4100]\ttraining's l1: 0.390316\tvalid_1's l1: 0.414608\n",
      "[4200]\ttraining's l1: 0.389707\tvalid_1's l1: 0.414564\n",
      "[4300]\ttraining's l1: 0.389088\tvalid_1's l1: 0.414547\n",
      "[4400]\ttraining's l1: 0.388483\tvalid_1's l1: 0.414499\n",
      "[4500]\ttraining's l1: 0.38786\tvalid_1's l1: 0.414458\n",
      "[4600]\ttraining's l1: 0.387288\tvalid_1's l1: 0.414443\n",
      "[4700]\ttraining's l1: 0.386708\tvalid_1's l1: 0.414452\n",
      "[4800]\ttraining's l1: 0.386107\tvalid_1's l1: 0.414418\n",
      "[4900]\ttraining's l1: 0.385553\tvalid_1's l1: 0.414392\n",
      "[5000]\ttraining's l1: 0.384973\tvalid_1's l1: 0.414369\n",
      "[5100]\ttraining's l1: 0.384414\tvalid_1's l1: 0.414356\n",
      "[5200]\ttraining's l1: 0.38384\tvalid_1's l1: 0.414315\n",
      "[5300]\ttraining's l1: 0.383301\tvalid_1's l1: 0.4143\n",
      "[5400]\ttraining's l1: 0.382748\tvalid_1's l1: 0.414283\n",
      "[5500]\ttraining's l1: 0.382177\tvalid_1's l1: 0.414249\n",
      "[5600]\ttraining's l1: 0.381621\tvalid_1's l1: 0.414236\n",
      "[5700]\ttraining's l1: 0.381071\tvalid_1's l1: 0.414211\n",
      "[5800]\ttraining's l1: 0.380541\tvalid_1's l1: 0.414207\n",
      "[5900]\ttraining's l1: 0.379986\tvalid_1's l1: 0.414205\n",
      "[6000]\ttraining's l1: 0.379444\tvalid_1's l1: 0.414198\n",
      "[6100]\ttraining's l1: 0.378895\tvalid_1's l1: 0.414199\n",
      "Early stopping, best iteration is:\n",
      "[6037]\ttraining's l1: 0.379243\tvalid_1's l1: 0.414186\n",
      "MSE: 3808795.2230344717\n",
      "Fold 4 finished in 0:02:04.086014\n",
      "Training on fold 5\n",
      "Training until validation scores don't improve for 150 rounds\n",
      "[100]\ttraining's l1: 0.517816\tvalid_1's l1: 0.519196\n",
      "[200]\ttraining's l1: 0.473672\tvalid_1's l1: 0.476078\n",
      "[300]\ttraining's l1: 0.45238\tvalid_1's l1: 0.455523\n",
      "[400]\ttraining's l1: 0.439845\tvalid_1's l1: 0.443776\n",
      "[500]\ttraining's l1: 0.43194\tvalid_1's l1: 0.436723\n",
      "[600]\ttraining's l1: 0.426654\tvalid_1's l1: 0.432215\n",
      "[700]\ttraining's l1: 0.422862\tvalid_1's l1: 0.429151\n",
      "[800]\ttraining's l1: 0.419974\tvalid_1's l1: 0.426992\n",
      "[900]\ttraining's l1: 0.417697\tvalid_1's l1: 0.425419\n",
      "[1000]\ttraining's l1: 0.415719\tvalid_1's l1: 0.42416\n",
      "[1100]\ttraining's l1: 0.414009\tvalid_1's l1: 0.42314\n",
      "[1200]\ttraining's l1: 0.412473\tvalid_1's l1: 0.422304\n",
      "[1300]\ttraining's l1: 0.411148\tvalid_1's l1: 0.421682\n",
      "[1400]\ttraining's l1: 0.409898\tvalid_1's l1: 0.421084\n",
      "[1500]\ttraining's l1: 0.408722\tvalid_1's l1: 0.420582\n",
      "[1600]\ttraining's l1: 0.407669\tvalid_1's l1: 0.420181\n",
      "[1700]\ttraining's l1: 0.406698\tvalid_1's l1: 0.419816\n",
      "[1800]\ttraining's l1: 0.405772\tvalid_1's l1: 0.41952\n",
      "[1900]\ttraining's l1: 0.404878\tvalid_1's l1: 0.419253\n",
      "[2000]\ttraining's l1: 0.404048\tvalid_1's l1: 0.419023\n",
      "[2100]\ttraining's l1: 0.403189\tvalid_1's l1: 0.418761\n",
      "[2200]\ttraining's l1: 0.402402\tvalid_1's l1: 0.418573\n",
      "[2300]\ttraining's l1: 0.40163\tvalid_1's l1: 0.418373\n",
      "[2400]\ttraining's l1: 0.40086\tvalid_1's l1: 0.418223\n",
      "[2500]\ttraining's l1: 0.400129\tvalid_1's l1: 0.418084\n",
      "[2600]\ttraining's l1: 0.399387\tvalid_1's l1: 0.417938\n",
      "[2700]\ttraining's l1: 0.398674\tvalid_1's l1: 0.417808\n",
      "[2800]\ttraining's l1: 0.397965\tvalid_1's l1: 0.417691\n",
      "[2900]\ttraining's l1: 0.397293\tvalid_1's l1: 0.417585\n",
      "[3000]\ttraining's l1: 0.396617\tvalid_1's l1: 0.417459\n",
      "[3100]\ttraining's l1: 0.395915\tvalid_1's l1: 0.417379\n",
      "[3200]\ttraining's l1: 0.395261\tvalid_1's l1: 0.417292\n",
      "[3300]\ttraining's l1: 0.39463\tvalid_1's l1: 0.417235\n",
      "[3400]\ttraining's l1: 0.393977\tvalid_1's l1: 0.417159\n",
      "[3500]\ttraining's l1: 0.393322\tvalid_1's l1: 0.417096\n",
      "[3600]\ttraining's l1: 0.392688\tvalid_1's l1: 0.417044\n",
      "[3700]\ttraining's l1: 0.392067\tvalid_1's l1: 0.416995\n",
      "[3800]\ttraining's l1: 0.391437\tvalid_1's l1: 0.416926\n",
      "[3900]\ttraining's l1: 0.390823\tvalid_1's l1: 0.416878\n",
      "[4000]\ttraining's l1: 0.390228\tvalid_1's l1: 0.416842\n",
      "[4100]\ttraining's l1: 0.389624\tvalid_1's l1: 0.416801\n",
      "[4200]\ttraining's l1: 0.389021\tvalid_1's l1: 0.416753\n",
      "[4300]\ttraining's l1: 0.388407\tvalid_1's l1: 0.416714\n",
      "[4400]\ttraining's l1: 0.387812\tvalid_1's l1: 0.416689\n",
      "[4500]\ttraining's l1: 0.387215\tvalid_1's l1: 0.416663\n",
      "[4600]\ttraining's l1: 0.386655\tvalid_1's l1: 0.416658\n",
      "[4700]\ttraining's l1: 0.386068\tvalid_1's l1: 0.416618\n",
      "[4800]\ttraining's l1: 0.385473\tvalid_1's l1: 0.416581\n",
      "[4900]\ttraining's l1: 0.384882\tvalid_1's l1: 0.416541\n",
      "[5000]\ttraining's l1: 0.384295\tvalid_1's l1: 0.416516\n",
      "[5100]\ttraining's l1: 0.383735\tvalid_1's l1: 0.416497\n",
      "[5200]\ttraining's l1: 0.383171\tvalid_1's l1: 0.416484\n",
      "[5300]\ttraining's l1: 0.382603\tvalid_1's l1: 0.416464\n",
      "[5400]\ttraining's l1: 0.382013\tvalid_1's l1: 0.416441\n",
      "[5500]\ttraining's l1: 0.381459\tvalid_1's l1: 0.416422\n",
      "[5600]\ttraining's l1: 0.380936\tvalid_1's l1: 0.416404\n",
      "[5700]\ttraining's l1: 0.380382\tvalid_1's l1: 0.416408\n",
      "[5800]\ttraining's l1: 0.37985\tvalid_1's l1: 0.416409\n",
      "Early stopping, best iteration is:\n",
      "[5727]\ttraining's l1: 0.380229\tvalid_1's l1: 0.416397\n",
      "MSE: 3631734.7044669446\n",
      "Fold 5 finished in 0:02:00.062677\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "   INFO ->  (188318, 666)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on fold 1\n",
      "[16:26:07] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.11144\n",
      "[100]\tvalidation_0-mae:2.60733\n",
      "[200]\tvalidation_0-mae:0.99443\n",
      "[300]\tvalidation_0-mae:0.55409\n",
      "[400]\tvalidation_0-mae:0.46410\n",
      "[500]\tvalidation_0-mae:0.44177\n",
      "[600]\tvalidation_0-mae:0.43347\n",
      "[700]\tvalidation_0-mae:0.42904\n",
      "[800]\tvalidation_0-mae:0.42635\n",
      "[900]\tvalidation_0-mae:0.42452\n",
      "[1000]\tvalidation_0-mae:0.42323\n",
      "[1100]\tvalidation_0-mae:0.42230\n",
      "[1200]\tvalidation_0-mae:0.42162\n",
      "[1300]\tvalidation_0-mae:0.42110\n",
      "[1400]\tvalidation_0-mae:0.42070\n",
      "[1500]\tvalidation_0-mae:0.42035\n",
      "[1600]\tvalidation_0-mae:0.42012\n",
      "[1700]\tvalidation_0-mae:0.41990\n",
      "[1800]\tvalidation_0-mae:0.41979\n",
      "[1900]\tvalidation_0-mae:0.41970\n",
      "[2000]\tvalidation_0-mae:0.41960\n",
      "[2100]\tvalidation_0-mae:0.41960\n",
      "[2200]\tvalidation_0-mae:0.41955\n",
      "[2300]\tvalidation_0-mae:0.41953\n",
      "[2400]\tvalidation_0-mae:0.41945\n",
      "[2497]\tvalidation_0-mae:0.41947\n",
      "MSE: 4064745.0\n",
      "Fold 1 finished in 0:06:39.315470\n",
      "Training on fold 2\n",
      "[16:32:43] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.12105\n",
      "[100]\tvalidation_0-mae:2.61765\n",
      "[200]\tvalidation_0-mae:1.00388\n",
      "[300]\tvalidation_0-mae:0.55841\n",
      "[400]\tvalidation_0-mae:0.46497\n",
      "[500]\tvalidation_0-mae:0.44135\n",
      "[600]\tvalidation_0-mae:0.43253\n",
      "[700]\tvalidation_0-mae:0.42787\n",
      "[800]\tvalidation_0-mae:0.42502\n",
      "[900]\tvalidation_0-mae:0.42328\n",
      "[1000]\tvalidation_0-mae:0.42203\n",
      "[1100]\tvalidation_0-mae:0.42113\n",
      "[1200]\tvalidation_0-mae:0.42050\n",
      "[1300]\tvalidation_0-mae:0.41998\n",
      "[1400]\tvalidation_0-mae:0.41962\n",
      "[1500]\tvalidation_0-mae:0.41934\n",
      "[1600]\tvalidation_0-mae:0.41917\n",
      "[1700]\tvalidation_0-mae:0.41900\n",
      "[1800]\tvalidation_0-mae:0.41890\n",
      "[1900]\tvalidation_0-mae:0.41885\n",
      "[2000]\tvalidation_0-mae:0.41880\n",
      "[2100]\tvalidation_0-mae:0.41874\n",
      "[2200]\tvalidation_0-mae:0.41864\n",
      "[2300]\tvalidation_0-mae:0.41861\n",
      "[2393]\tvalidation_0-mae:0.41863\n",
      "MSE: 4079331.0\n",
      "Fold 2 finished in 0:06:18.152725\n",
      "Training on fold 3\n",
      "[16:39:01] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.11687\n",
      "[100]\tvalidation_0-mae:2.61017\n",
      "[200]\tvalidation_0-mae:0.99520\n",
      "[300]\tvalidation_0-mae:0.54942\n",
      "[400]\tvalidation_0-mae:0.45614\n",
      "[500]\tvalidation_0-mae:0.43306\n",
      "[600]\tvalidation_0-mae:0.42458\n",
      "[700]\tvalidation_0-mae:0.42050\n",
      "[800]\tvalidation_0-mae:0.41799\n",
      "[900]\tvalidation_0-mae:0.41636\n",
      "[1000]\tvalidation_0-mae:0.41522\n",
      "[1100]\tvalidation_0-mae:0.41439\n",
      "[1200]\tvalidation_0-mae:0.41375\n",
      "[1300]\tvalidation_0-mae:0.41332\n",
      "[1400]\tvalidation_0-mae:0.41289\n",
      "[1500]\tvalidation_0-mae:0.41258\n",
      "[1600]\tvalidation_0-mae:0.41236\n",
      "[1700]\tvalidation_0-mae:0.41212\n",
      "[1800]\tvalidation_0-mae:0.41201\n",
      "[1900]\tvalidation_0-mae:0.41189\n",
      "[2000]\tvalidation_0-mae:0.41182\n",
      "[2100]\tvalidation_0-mae:0.41173\n",
      "[2200]\tvalidation_0-mae:0.41168\n",
      "[2283]\tvalidation_0-mae:0.41174\n",
      "MSE: 3408030.25\n",
      "Fold 3 finished in 0:05:56.502373\n",
      "Training on fold 4\n",
      "[16:44:57] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.10715\n",
      "[100]\tvalidation_0-mae:2.60207\n",
      "[200]\tvalidation_0-mae:0.99088\n",
      "[300]\tvalidation_0-mae:0.54984\n",
      "[400]\tvalidation_0-mae:0.46204\n",
      "[500]\tvalidation_0-mae:0.44018\n",
      "[600]\tvalidation_0-mae:0.43208\n",
      "[700]\tvalidation_0-mae:0.42795\n",
      "[800]\tvalidation_0-mae:0.42539\n",
      "[900]\tvalidation_0-mae:0.42359\n",
      "[1000]\tvalidation_0-mae:0.42234\n",
      "[1100]\tvalidation_0-mae:0.42149\n",
      "[1200]\tvalidation_0-mae:0.42075\n",
      "[1300]\tvalidation_0-mae:0.42024\n",
      "[1400]\tvalidation_0-mae:0.41979\n",
      "[1500]\tvalidation_0-mae:0.41946\n",
      "[1600]\tvalidation_0-mae:0.41922\n",
      "[1700]\tvalidation_0-mae:0.41899\n",
      "[1800]\tvalidation_0-mae:0.41880\n",
      "[1900]\tvalidation_0-mae:0.41866\n",
      "[2000]\tvalidation_0-mae:0.41861\n",
      "[2100]\tvalidation_0-mae:0.41857\n",
      "[2200]\tvalidation_0-mae:0.41849\n",
      "[2300]\tvalidation_0-mae:0.41845\n",
      "[2400]\tvalidation_0-mae:0.41842\n",
      "[2500]\tvalidation_0-mae:0.41835\n",
      "[2587]\tvalidation_0-mae:0.41839\n",
      "MSE: 3749578.75\n",
      "Fold 4 finished in 0:06:54.137998\n",
      "Training on fold 5\n",
      "[16:51:52] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.11362\n",
      "[100]\tvalidation_0-mae:2.60677\n",
      "[200]\tvalidation_0-mae:0.99284\n",
      "[300]\tvalidation_0-mae:0.54952\n",
      "[400]\tvalidation_0-mae:0.45905\n",
      "[500]\tvalidation_0-mae:0.43754\n",
      "[600]\tvalidation_0-mae:0.42979\n",
      "[700]\tvalidation_0-mae:0.42591\n",
      "[800]\tvalidation_0-mae:0.42349\n",
      "[900]\tvalidation_0-mae:0.42187\n",
      "[1000]\tvalidation_0-mae:0.42077\n",
      "[1100]\tvalidation_0-mae:0.41994\n",
      "[1200]\tvalidation_0-mae:0.41935\n",
      "[1300]\tvalidation_0-mae:0.41892\n",
      "[1400]\tvalidation_0-mae:0.41851\n",
      "[1500]\tvalidation_0-mae:0.41819\n",
      "[1600]\tvalidation_0-mae:0.41798\n",
      "[1700]\tvalidation_0-mae:0.41780\n",
      "[1800]\tvalidation_0-mae:0.41767\n",
      "[1900]\tvalidation_0-mae:0.41754\n",
      "[2000]\tvalidation_0-mae:0.41746\n",
      "[2100]\tvalidation_0-mae:0.41741\n",
      "[2200]\tvalidation_0-mae:0.41735\n",
      "[2300]\tvalidation_0-mae:0.41732\n",
      "[2400]\tvalidation_0-mae:0.41732\n",
      "[2435]\tvalidation_0-mae:0.41733\n",
      "MSE: 3602720.0\n",
      "Fold 5 finished in 0:06:26.805836\n",
      "Training on fold 6\n",
      "[16:58:18] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.11604\n",
      "[100]\tvalidation_0-mae:2.61046\n",
      "[200]\tvalidation_0-mae:0.99849\n",
      "[300]\tvalidation_0-mae:0.55421\n",
      "[400]\tvalidation_0-mae:0.46225\n",
      "[500]\tvalidation_0-mae:0.43894\n",
      "[600]\tvalidation_0-mae:0.43043\n",
      "[700]\tvalidation_0-mae:0.42608\n",
      "[800]\tvalidation_0-mae:0.42350\n",
      "[900]\tvalidation_0-mae:0.42182\n",
      "[1000]\tvalidation_0-mae:0.42060\n",
      "[1100]\tvalidation_0-mae:0.41966\n",
      "[1200]\tvalidation_0-mae:0.41897\n",
      "[1300]\tvalidation_0-mae:0.41852\n",
      "[1400]\tvalidation_0-mae:0.41809\n",
      "[1500]\tvalidation_0-mae:0.41780\n",
      "[1600]\tvalidation_0-mae:0.41752\n",
      "[1700]\tvalidation_0-mae:0.41734\n",
      "[1800]\tvalidation_0-mae:0.41724\n",
      "[1900]\tvalidation_0-mae:0.41712\n",
      "[2000]\tvalidation_0-mae:0.41702\n",
      "[2100]\tvalidation_0-mae:0.41696\n",
      "[2200]\tvalidation_0-mae:0.41696\n",
      "[2300]\tvalidation_0-mae:0.41690\n",
      "[2400]\tvalidation_0-mae:0.41690\n",
      "[2437]\tvalidation_0-mae:0.41687\n",
      "MSE: 3923994.0\n",
      "Fold 6 finished in 0:06:27.688949\n",
      "Training on fold 7\n",
      "[17:04:46] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.10666\n",
      "[100]\tvalidation_0-mae:2.60383\n",
      "[200]\tvalidation_0-mae:0.99099\n",
      "[300]\tvalidation_0-mae:0.55216\n",
      "[400]\tvalidation_0-mae:0.46368\n",
      "[500]\tvalidation_0-mae:0.44079\n",
      "[600]\tvalidation_0-mae:0.43223\n",
      "[700]\tvalidation_0-mae:0.42765\n",
      "[800]\tvalidation_0-mae:0.42499\n",
      "[900]\tvalidation_0-mae:0.42317\n",
      "[1000]\tvalidation_0-mae:0.42187\n",
      "[1100]\tvalidation_0-mae:0.42097\n",
      "[1200]\tvalidation_0-mae:0.42027\n",
      "[1300]\tvalidation_0-mae:0.41977\n",
      "[1400]\tvalidation_0-mae:0.41935\n",
      "[1500]\tvalidation_0-mae:0.41908\n",
      "[1600]\tvalidation_0-mae:0.41887\n",
      "[1700]\tvalidation_0-mae:0.41873\n",
      "[1800]\tvalidation_0-mae:0.41862\n",
      "[1900]\tvalidation_0-mae:0.41848\n",
      "[2000]\tvalidation_0-mae:0.41846\n",
      "[2100]\tvalidation_0-mae:0.41847\n",
      "[2112]\tvalidation_0-mae:0.41846\n",
      "MSE: 3601296.75\n",
      "Fold 7 finished in 0:05:26.202025\n",
      "Training on fold 8\n",
      "[17:10:12] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.11544\n",
      "[100]\tvalidation_0-mae:2.60915\n",
      "[200]\tvalidation_0-mae:0.99423\n",
      "[300]\tvalidation_0-mae:0.55034\n",
      "[400]\tvalidation_0-mae:0.45869\n",
      "[500]\tvalidation_0-mae:0.43546\n",
      "[600]\tvalidation_0-mae:0.42700\n",
      "[700]\tvalidation_0-mae:0.42270\n",
      "[800]\tvalidation_0-mae:0.42024\n",
      "[900]\tvalidation_0-mae:0.41865\n",
      "[1000]\tvalidation_0-mae:0.41747\n",
      "[1100]\tvalidation_0-mae:0.41658\n",
      "[1200]\tvalidation_0-mae:0.41597\n",
      "[1300]\tvalidation_0-mae:0.41555\n",
      "[1400]\tvalidation_0-mae:0.41525\n",
      "[1500]\tvalidation_0-mae:0.41495\n",
      "[1600]\tvalidation_0-mae:0.41478\n",
      "[1700]\tvalidation_0-mae:0.41471\n",
      "[1800]\tvalidation_0-mae:0.41462\n",
      "[1900]\tvalidation_0-mae:0.41457\n",
      "[2000]\tvalidation_0-mae:0.41450\n",
      "[2100]\tvalidation_0-mae:0.41453\n",
      "[2138]\tvalidation_0-mae:0.41455\n",
      "MSE: 4213662.5\n",
      "Fold 8 finished in 0:05:31.227847\n",
      "Training on fold 9\n",
      "[17:15:43] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.11395\n",
      "[100]\tvalidation_0-mae:2.60973\n",
      "[200]\tvalidation_0-mae:0.99623\n",
      "[300]\tvalidation_0-mae:0.55039\n",
      "[400]\tvalidation_0-mae:0.45784\n",
      "[500]\tvalidation_0-mae:0.43526\n",
      "[600]\tvalidation_0-mae:0.42674\n",
      "[700]\tvalidation_0-mae:0.42228\n",
      "[800]\tvalidation_0-mae:0.41970\n",
      "[900]\tvalidation_0-mae:0.41790\n",
      "[1000]\tvalidation_0-mae:0.41671\n",
      "[1100]\tvalidation_0-mae:0.41575\n",
      "[1200]\tvalidation_0-mae:0.41509\n",
      "[1300]\tvalidation_0-mae:0.41451\n",
      "[1400]\tvalidation_0-mae:0.41412\n",
      "[1500]\tvalidation_0-mae:0.41384\n",
      "[1600]\tvalidation_0-mae:0.41360\n",
      "[1700]\tvalidation_0-mae:0.41340\n",
      "[1800]\tvalidation_0-mae:0.41330\n",
      "[1900]\tvalidation_0-mae:0.41320\n",
      "[2000]\tvalidation_0-mae:0.41312\n",
      "[2100]\tvalidation_0-mae:0.41310\n",
      "[2168]\tvalidation_0-mae:0.41308\n",
      "MSE: 4344547.5\n",
      "Fold 9 finished in 0:05:37.097504\n",
      "Training on fold 10\n",
      "[17:21:20] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose_eval } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[0]\tvalidation_0-mae:7.11888\n",
      "[100]\tvalidation_0-mae:2.61342\n",
      "[200]\tvalidation_0-mae:0.99706\n",
      "[300]\tvalidation_0-mae:0.55267\n",
      "[400]\tvalidation_0-mae:0.46194\n",
      "[500]\tvalidation_0-mae:0.43920\n",
      "[600]\tvalidation_0-mae:0.43056\n",
      "[700]\tvalidation_0-mae:0.42613\n",
      "[800]\tvalidation_0-mae:0.42341\n",
      "[900]\tvalidation_0-mae:0.42168\n",
      "[1000]\tvalidation_0-mae:0.42044\n",
      "[1100]\tvalidation_0-mae:0.41952\n",
      "[1200]\tvalidation_0-mae:0.41885\n",
      "[1300]\tvalidation_0-mae:0.41837\n",
      "[1400]\tvalidation_0-mae:0.41798\n",
      "[1500]\tvalidation_0-mae:0.41763\n",
      "[1600]\tvalidation_0-mae:0.41740\n",
      "[1700]\tvalidation_0-mae:0.41725\n",
      "[1800]\tvalidation_0-mae:0.41717\n",
      "[1900]\tvalidation_0-mae:0.41705\n",
      "[2000]\tvalidation_0-mae:0.41701\n",
      "[2100]\tvalidation_0-mae:0.41700\n",
      "[2200]\tvalidation_0-mae:0.41697\n",
      "[2285]\tvalidation_0-mae:0.41697\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "   INFO ->  Average KFold RMSE: 3890520.0\n",
      "   INFO ->  feature importance\n",
      "   INFO ->                    feature  fold_1  fold_2  fold_3  fold_4  fold_5  average\n",
      "0                  cat100    2310    2130    2208    2171    2205   2204.8\n",
      "1    cat112__cont14__rank    1978    1635    1797    1640    1489   1707.8\n",
      "2            COUNT_cat100    1358    1256    1307    1405    1205   1306.2\n",
      "3                  cat112    1308    1125    1225    1213    1062   1186.6\n",
      "4                  cont14    1099    1085    1053    1154     992   1076.6\n",
      "..                    ...     ...     ...     ...     ...     ...      ...\n",
      "661           COUNT_cat69       1       0       2       0       0      0.6\n",
      "662                 cat20       0       0       0       0       1      0.2\n",
      "663           COUNT_cat20       0       0       0       0       0      0.0\n",
      "664                 cat70       0       0       0       0       0      0.0\n",
      "665           COUNT_cat70       0       0       0       0       0      0.0\n",
      "\n",
      "[666 rows x 7 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 3917296.5\n",
      "Fold 10 finished in 0:05:57.654380\n"
     ]
    }
   ],
   "source": [
    "sub = autox.get_submit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8f36152",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-19T09:28:05.784646Z",
     "start_time": "2021-10-19T09:28:05.512342Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sub.to_csv(\"autox_1019_Allstate_oneclick.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc70220d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-10-19T09:28:05.789504Z",
     "start_time": "2021-10-19T09:28:05.786957Z"
    }
   },
   "outputs": [],
   "source": [
    "# !zip -r autox_0927_kaggle_ventilator_一键执行.csv.zip autox_0927_kaggle_ventilator_一键执行.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3d537a6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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